Take your AI skills to the next level with intermediate courses. Build on foundational knowledge with hands-on projects, deeper algorithms, and real-world applications.
A comprehensive bootcamp for aspiring AI engineers that covers key AI concepts, Python programming for AI, and leveraging powerful tools like LangChain and Hugging Face. The course includes real-life business case studies.
A concise, hands-on course on the fundamentals of synthetic data, its applications, and key generation techniques including statistical methods and generative AI approaches like GANs and VAEs.
Top-down approach to deep learning using the fastai library. Build state-of-the-art models without needing a PhD.
Complete data science workflow specialization covering data cleaning, analysis, visualization, and machine learning applications.
Master the fundamentals of machine learning with this comprehensive course from Stanford University
A course focused on using AI for coding, specifically covering tools like Cursor and Claude Code for full-stack development and co-coding with AI agents.
Learn Hebrew University of JerusalemBuild a Modern Computer from First Principles: From Nand to Tetris (Project-Centered Course)Course
This course focuses on creating and evaluating human data for SFT, RLHF, and red-teaming. It also covers mastering AI data quality and safety standards, which is a crucial aspect of data curation for AI models.
This course combines Data Science, Machine Learning, and Deep Learning to create powerful AI for real-world cybersecurity applications. Topics covered include Isolation Forest, Markov Chains, NLP, and various regression and classification algorithms. Students will learn to use tools like Pandas, Numpy, Tensorflow, and Scikit-Learn.
A comprehensive, hands-on guide to Tableau for data science, covering all the essential skills for creating powerful visualizations for EDA.
Learn Transformer models and BERT model: Overview
Learn Edge AI Development
Learn Ethics in AI
A four-week course that equips learners with tools and strategies to mitigate the climate impact of AI, optimize AI products for sustainability, and implement responsible, energy-efficient AI technologies. The course covers sustainable AI principles, measurement tools, and the ROI of adopting sustainable AI practices.
This course equips UX and UI designers with an understanding of AI's capabilities and limitations, providing practical guidance on integrating AI-powered tools into the design workflow to enhance productivity and creativity.
This course is designed for finance professionals and business leaders to integrate AI into budgeting and cost optimization strategies. It covers leveraging AI for real-time insights, dynamic cost management, and enhanced forecasting accuracy.
Text classification, sentiment analysis, topic modeling, text generation with spaCy, NLTK.
This course focuses on building and deploying AI agents for various tasks. While not exclusively about document intelligence, several projects involve creating agents for research and data extraction, which are relevant to building OCR pipelines. It covers frameworks like CrewAI and LangGraph.
A comprehensive guide to over 50 generative AI tools to enhance business productivity and creativity, with a focus on ChatGPT and prompt engineering.
A comprehensive course on Udemy that covers building, training, and deploying machine learning models using Microsoft Azure ML Studio, including no-code and Python-based approaches. It covers AutoML as a key component of the Azure ML platform.
THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON!It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using two of the most important Deep Learning frameworks- Tensorflow and Keras. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python.. This means, this course covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning... By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level.THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TENSORFLOW BASED DATA SCIENCE!But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably wit
Atenção! Nas aulas deste curso é utilizada a versão 1.x do TensorFlow, sendo possível acompanhar as aulas utilizando essa versão. Adicionalmente, disponibilizamos o código atualizado considerando a versão 2.x. Em breve pretendemos regravar todas as aulas deste cursoA área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, eBay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o
Welcome to my " Complete Python for Data Science & Machine Learning from A-Z " course.Python with Machine Learning & Data Science, Data Visulation, Numpy & Pandas for Data Analysis, Kaggle projects from A-Z Python is a computer programming language often used to build websites and software, automate tasks, and conduct data analysis. Python is a general-purpose language, meaning it can be used to create a variety of different programs and isn't specialized for any specific problems.Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn.Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.Do you want to learn one of the employer’s most requested skills? If you think so, you are at the right place. Python, machine learning, Django, python programming, machine learning python, python Bootcamp, coding, data science, data analysis, programming languages.We've designed for you "Complete Python for Data Science & Machine Learning from A-Z” a straightforward course for the Complete Python programming langu
A course focused on building Generative AI SaaS applications with tools like ChatGPT, MongoDB, Firebase, and Stripe, with a claim of no coding skills required. It covers building and testing mobile apps using Expo Snack.
Pada kursus ini, teman-teman akan belajar mengenai pengolahan citra dengan menggunakan Bahasa Python. Materi pada kursus ini didesain sesederhana mungkin agar teman-teman dapat lebih mudah dalam memahami materi yang disampaikan. Selain materi yang mudah dipahami dan dipelajari, materi pada kursus ini akan dikembangkan dan ditambahkan secara terus menerus seiring berkembangnya bidang computer vision atau pengolahan citra. Materi yang disajikan berawal dari materi paling sederhana yaitu pre-processing citra dan dilanjutkan dengan deep learning.Pada pre-processing citra, teman-teman akan belajar mengenai rotasi, shifting(pergeseran pixel), flipping, ruang warna dan masih banyak lagi. Pada materi ruang warna, teman-teman akan belajar juga mengenai perhitungan matematika secara manual sebelum implementasi dengan menggunakan python. Pada materi deep learning, teman-teman akan belajar mengenai Neural Network atau NN dan Convolutional Neural Network (CNN). Materi yang akan dipelajari pada Neural Network berupa perhitungan matematika dari forward pass dan backward pass. Selain perhitungan manual, teman-teman juga akan belajar bagaimana cara mengimplementasikan Neural Network dengan menggunakan Bahasa Python dengan library Pytorch. Pada materi Convolutional Neural Network, teman-teman akan mempelajari bagaimana sebuah mesin mempelajar sebuah data dan membuat sebuah sistem Artificial Intelligence (AI) secara sederhana. Materi Convolutional Neural Network yang disajikan antara lain, bagaimana penerapan dengan menggunakan Bahasa Python dengan library Pytorch dan bagaimana contoh-contoh penggunaan Convolutional Neural Network dalam kehidupan sehari-hari.
A visual introduction to neural networks and deep learning. This series explains the fundamentals of neural networks with beautiful animations and intuitive explanations.
A bestselling bootcamp that teaches practical skills for working professionally with AI, including GPT-4, Midjourney, and GitHub Copilot. It covers the 'Five Principles of Prompting' and other professional-grade tips and tricks.
Learn AWS Machine Learning and AI Complete Course
A guided project on Coursera that focuses on using the powerful BERT model for sentiment analysis tasks.
This course provides a comprehensive understanding of the theory behind Support Vector Machines, including the derivation of Linear SVM, the Kernel SVM using Lagrangian Duality, and the application of Quadratic Programming. It covers practical applications like image recognition and spam detection.
This corporate training program equips employees with practical knowledge of applying AI to personalize experiences, optimize inventory management, and automate operations in retail and e-commerce.
This course focuses on Claude as a powerful alternative to ChatGPT, teaching users how to leverage its advantages for marketing, business, content creation, and market research.
Selamat datang di program Pelatihan Data Science dengan Deep Learning dan PythonPelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dengan titik fokus pemanfaatan Deep Learning untuk model machine learning dan data science.Peserta diharapkan sudah menguasai pemrograman Python dasar implementasi machine learning dan data science dengan menggunakan Python. Kami juga menyediakan konten mengenai Pelatihan Data Science dan Machine Learning Dengan Python yang ada di Udemy ini.Seluruh konten didalam pelatihan ini dilaksanan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihanKonsep dan teori mengenai Deep LearningPengenalan TensorFlow dan KerasDasar Tensor TensorFlow Pemanfaatan GPU dan TPU pada komputasi TensorFlow dan KerasPembuat Model dan Layer Untuk TensorFlowTraining dan evaluasi Deep Learning pada TensorFlowPengenalan dan instalasi PyTorchPemanfaatan GPU dan TPU pada komputasi PyTorchMembangun model Deep Learning dengan PyTorchTraining dan evaluasi Deep Learning pada PyTorchPenggunaan TensorBoard untuk visualisasi model pada TensorFlow dan PyTorchPenerapan Hyperparameter Tuning pada TensorFlow dan KerasPenerapan Hyperparameter Tuning pada PyTorchPenggunaan TensorBoard untuk implementasi HyperparameterKumpulan Studi KasusJika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada we
A project-based course where you will build and train a bidirectional LSTM neural network model to recognize named entities in text data using Keras with a TensorFlow backend. This is a key tool for information extraction and a preprocessing step for other NLP applications.
This course focuses on integrating generative AI into the UI/UX design workflow. It covers using AI for tasks like creating personas and journey maps while ensuring the design remains human-centered, inclusive, and accessible.
This course provides practical skills in using Python and the scikit-learn library for machine learning, with a focus on supervised learning.
This course focuses on how AI can optimize demand forecasting, support risk mitigation strategies, and drive automation within supply chain operations through real-world examples and hands-on exercises.
Learn Machine Learning and AI Foundations: Value Estimations
Learn ChatGPT & OpenAI API Development Masterclass
As aplicações de Inteligência Artificial (IA) com Python têm desempenhado um papel significativo no setor financeiro, trazendo uma série de benefícios e transformando a forma como as instituições lidam com dados e tomam decisões. Aqui está um resumo da importância dessas aplicações em finanças:1. Tomada de Decisão Baseada em Dados: - A IA com Python capacita as instituições financeiras a tomar decisões mais informadas e precisas, utilizando algoritmos avançados para analisar grandes conjuntos de dados. Isso resulta em estratégias mais eficazes de investimento, gestão de riscos aprimorada e decisões mais fundamentadas.2. Previsão de Mercado e Tendências: - Algoritmos de machine learning e modelos de IA são utilizados para prever movimentos de mercado, identificar tendências e realizar análises preditivas. Isso auxilia investidores, traders e gestores de ativos na identificação de oportunidades e na mitigação de riscos.3. Detecção de Fraudes e Segurança: - Sistemas de IA são empregados para detectar padrões suspeitos e atividades fraudulentas em transações financeiras. Essa capacidade de análise em tempo real contribui para a segurança das transações e a proteção contra atividades fraudulentas.4. Gestão de Portfólio Automatizada: - Algoritmos de IA e aprendizado de máquina são usados para criar e otimizar automaticamente portfólios de investimento. Esses sistemas automatizados podem ajustar dinamicamente as alocações de ativos com base em condições de mercado em constante mudança.5. Atendimento ao Cliente e Chatbots: - A IA é aplicada em chatbots e assistentes virtuais para melhorar o atendimento ao cliente. Essas soluções são capazes de responder a consultas, fornecer informações sobre contas e até mesmo realizar transações simples, melhorando a eficiência e a experiência do cliente.6. Análise de Sentimento e Mí
This comprehensive lesson teaches how to test ML artifacts including code, data, and models to build a reliable ML system. It covers the intuition behind testing, different types of tests (unit, integration, system, acceptance, regression), best practices, and implementation details for testing code, data expectations, and model behavior.
Learn SQL for Data Science and Machine Learning
A free program designed to equip senior government leaders with foundational AI knowledge, including cohort-based learning, executive coaching, and an applied use case project.
Welcome to the Complete Deep Learning Course 2021 With 7+ Real ProjectsThis course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!This course is designed to balance theory and practical implementation, with complete google colab and Jupiter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way!This course covers a variety of topics, includingDeep Learning.Google ColabAnacondaJupiter NotebookActivation Function.Keras.Pandas.Seaborn.Feature scaling.Matplotlib.scikit-learnSigmoid Function.Tanh Function.ReLU Function.Leaky Relu Function.Exponential Linear Unit Function.Swish function.Corpora.NLTK.TensorFlow 2.0Tokenization.Spacy.PoS tagging.NER.Stemming and lemmatization.Semantics and topic modelling.Sentiment analysis techniques.Lexicon-based methods.Rule-based methods.Statistical methods.Machine learning methods.</
Get up to speed with the latest AI research. Each video explains a cutting-edge paper in an accessible way.
Learn DeepLearning.AINeural Networks and Deep LearningCourse
Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more? Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250+ lectures, more than 25+ hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes.We are going to execute following real-life projects,Kaggle Bike Demand Prediction from Kaggle competitionAutomation of the Loan Approval processThe famous IRIS ClassificationAdult Income Predictions from US Census DatasetBank Telemarketing PredictionsBreast Cancer PredictionsPredict Diabetes using Prima Indians Diabetes DatasetToday Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others. As the Data Science and Machine Learning practioner, you will have to research and look beyond normal problems, you may need to do extensive data processing. experiment with the data using advance tools and build amazing solutions for business. However, where and how are you going to learn these skills required for Data Science and Machine Learning? Data Science and Machine Learning require in-depth knowledge of various topics. Data Science is not just about knowing certain packages/libraries and learning how to apply them. Data Science and Machine Learning require an indepth understanding of the following skills,Understanding of the overall landscape of Data Science and Machine LearningDifferent types of Data Analy
A comprehensive guide to understanding and implementing tree-based models and ensemble techniques in Python. The course covers Decision Trees, Random Forests, Bagging, AdaBoost, and XGBoost.
Learn Chatbot Development with Python and Deep Learning
This course explores the transformative potential of generative AI technologies in the realm of financial operations (FinOps). It covers the intricacies of large language models (LLMs) and how they can be harnessed to augment and optimize FinOps processes.
Industry-focused machine learning course with real-world projects and deployment strategies.
This masterclass provides a broad perspective on managing generative AI systems. It includes hands-on experience deploying HuggingFace and OpenAI models with a focus on monitoring, cost optimization, and automation pipelines. It also covers version control with Git and CI/CD demonstrations.
Learn IBMWhat is Data Science?Course
This program covers building and applying Generative AI techniques. For retail and e-commerce, it discusses enabling personalized recommendations and chatbot support.
This bootcamp on Udemy focuses on the operational aspects of deploying large language models. It covers CI/CD, Docker, Kubernetes, and monitoring for production LLM deployment, which are essential skills for managing and optimizing the cost and performance of LLMs at scale.
Learn DeepLearning.AIBuild Basic Generative Adversarial Networks (GANs)Course
This course focuses on the foundational concepts of LLMs, specifically tokenization and word embedding models. It includes practical, hands-on exercises for building and training these models using Pytorch.
Greetings, I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in-demand and most satisfying career, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see the impact of your work around your, is not is amazing?This is one of the most comprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making. Data Science Bootcamps are costly, in thousands of dollars. However, this course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with detailed code notebooks for every lecture. The course also includes practice exercises on real data for each topic you cover, because the goal is "Learn by Doing"! For your satisfaction, I would like to mention few topics that we will be learning in this course:Basis Python programming for Data ScienceData Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and FilterNumPyArrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal FunctionsPandasPandas Data Structures - Series, DataF
This course offers a strategic perspective on integrating AI into marketing. It focuses on how AI can transform marketing tactics through data analysis, predictive analytics, and personalized campaigns.
Learn LangChain for LLM Application Development
Learn Copilot Studio Development
Learn Imperial College LondonMathematics for Machine Learning: Linear AlgebraCourse
This course explores the intersection of AI, ethics, and psychology, including topics like emotional AI and the psychological impact of human-machine interaction.
Learn PyTorch for Deep Learning and Computer Vision
This course covers the range of ways that AI can be used to better perform common tasks within the logistics business, including inventory management and demand prediction.
This course is designed to share insight into the application of artificial intelligence tools and techniques to analyse and predict performance across various sports. It uses sport as the vehicle to learn how data and AI can be used to “measure the immeasurable”.
Complete Stanford CS229 Machine Learning course by Andrew Ng. Covers supervised learning, unsupervised learning, and best practices.
An online workshop on causal modeling and inference within a machine learning context, designed for self-paced learning.
Learn Status: FreeFreePrinceton UniversityAlgorithms, Part ICourse
Hi.. Hello and welcome to my new course, Machine Learning with Python for Dummies. We will discuss about the overview of the course and the contents included in this course.Artificial Intelligence, Machine Learning and Deep Learning Neural Networks are the most used terms now a days in the technology world. Its also the most mis-understood and confused terms too.Artificial Intelligence is a broad spectrum of science which tries to make machines intelligent like humans. Machine Learning and Neural Networks are two subsets that comes under this vast machine learning platformLets check what's machine learning now. Just like we human babies, we were actually in our learning phase then. We learned how to crawl, stand, walk, then speak words, then make simple sentences.. We learned from our experiences. We had many trials and errors before we learned how to walk and talk. The best trials for walking and talking which gave positive results were kept in our memory and made use later. This process is highly compared to a Machine Learning MechanismThen we grew young and started thinking logically about many things, had emotional feelings, etc. We kept on thinking and found solutions to problems in our daily life. That's what the Deep Learning Neural Network Scientists are trying to achieve. A thinking machine.But in this course we are focusing mainly in Machine Learning. Throughout this course, we are preparing our machine to make it ready for a prediction test. Its Just like how you prepare for your Mathematics Test in school or college. We learn and train ourselves by solving the most possible number of similar mathematical problems. Lets call these sample data of similar problems and their solutions as the 'Training Input' and 'Training Output' Respectively. And then the day comes when we have the actual test. We will be given new set of problems to solve, but very similar to the problems we learned, and based on th
This certification prepares project managers to integrate and apply AI in their workflows to enhance decision-making and optimize project outcomes. It is globally recognized and suitable for various project management roles without requiring formal education or work experience.
A self-paced course with lectures from Stanford faculty that helps public servants understand the opportunities and impact of AI in government, including its technology, capabilities, and risks.
Comprehensive Course Description:Electrification was undeniably one of the greatest engineering feats of the 20th century. The invention of the electric motor dates back to 1821, with mathematical analysis of electrical circuits following in 1827. However, it took several decades for the full electrification of factories, households, and railways to begin. Fast forward to today, and we are witnessing a similar trajectory with Artificial Intelligence (AI). Despite being formally founded in 1956, AI has only recently begun to revolutionize the way humanity lives and works.Similarly, Data Science is a vast and expanding field that encompasses data systems and processes aimed at organizing and deriving insights from data. One of the most important branches of AI, Machine Learning (ML), involves developing systems that can autonomously learn and improve from experience without human intervention. ML is at the forefront of AI, as it aims to endow machines with independent learning capabilities.Our "Data Science & Machine Learning Full Course in 90 Hours" offers an exhaustive exploration of both data science and machine learning, providing in-depth coverage of essential concepts in these fields. In today's world, organizations generate staggering amounts of data, and the ability to store, analyze, and derive meaningful insights from this data is invaluable. Data science plays a critical role here, focusing on data modeling, warehousing, and deriving practical outcomes from raw data.For data scientists, AI and ML are indispensable, as they not only help tackle large data sets but also enhance decision-making processes. The ability to transition between roles and apply these methodologies across different stages of a data science project makes them invaluable to any organization.What Makes This Course Unique?This course is designed to provide both theoretical foundations and practical, hands-on experience. By the end of the
Learn DeepLearning.AIGenerative AI for EveryoneCourse
Learn Building Systems with the ChatGPT API
Learn DeepLearning.AIImproving Deep Neural Networks: Hyperparameter Tuning, Regularization and OptimizationCourse
Learn AutoML: Automated Machine Learning
This program focuses on utilizing modern technologies to improve the performance and quality of manufacturing processes. Participants will learn to integrate artificial intelligence into quality management strategies, use AI to analyze data, enhance quality control processes, and develop analytical models to predict problems.
This certification validates the necessary knowledge and skills to apply prompt engineering techniques in various contexts. The course content and exam are free.
Learn Introduction to Data Science with Python
Programmer en Python pour la Data Science, le Machine Learning, la DataViz et l'Intelligence ArtificielleCe cours a pour objectif de vous initier à la programmation en Python en lien avec les concepts essentiels du Big Data (Data Science, Machine Learning, IA, etc.). Il ne requiert aucun prérequis et vous permet d'atteindre un niveau solide en seulement 4 heures de formation.Acquérir des bases solidesPlus besoin de partir à la chasse aux informations sur Google, l'essentiel de votre apprentissage est concentré dans ce cours.Gagner du tempsCe cours est conçu pour vous familiariser avec la Data Science et Python de manière rapide et efficace. Vous pourrez ainsi atteindre un niveau solide en seulement 4 heures de cours.Une formation qui va à votre rythmeLes concepts sont présentés progressivement, à travers des exemples concrets issus de projets d'entreprises et d'universités, vous permettant d'appliquer ce que vous avez appris.Cours récent et régulièrement mis à jourMis à jour récemment, ce cours est en adéquation avec les compétences actuellement recherchées par les entreprises.Éviter les pièges de débutantsCe cours détaille les bonnes pratiques d'un Data Scientist expérimenté pour rédiger un code de qualité professionnelle.Préparation réussie pour vos examens, certifications et tests techniques sur PythonLes exercices inclus dans ce cours constituent un excellent moyen de préparation pour vos examens, certifications et tests techniques en entreprise.Travailler pour les plus grandes entreprisesDes entreprises prestigieuses telles qu'Intel, Google, Netflix, Spotify, Meta, mais aussi Renault, la SNCF, Orange, Total, Capgemini, sont actuellement à la recherche de Data Scientists expérimentés maîtrisant Python.Se former à des métiers actuellement recherchés</stron
This course covers the theory behind support vector machines and how to implement and optimize a Support Vector Classifier in Python using sk-learn.
A comprehensive certificate program that equips professionals to lead the safe, secure, and responsible development and deployment of AI systems. The course covers the full AI lifecycle from generative AI fundamentals and architecture to governance, risk management, privacy, and cloud security through 10 modules.
A series of introductory online courses to gain a high-level understanding of how AI is optimizing risk management and insurance across various job functions like claims and underwriting.
Learn Machine Learning for Business Analytics
A free, self-paced online learning series to help educators understand and integrate AI into their teaching. Developed in partnership with ETS, ISTE, and Khan Academy, it demystifies AI, explores responsible implementation, and addresses bias.
Learn Intelligence Artificielle et Apprentissage Automatique
Learn AI for Product Managers
Jetzt neu: Zusätzlicher Bonus zum Thema Deep Learning (Neuronale Netze) mit Python, Tensorflow und Keras!Dieser Kurs enthält über 300 Lektionen, Quizze, Praxisbeispiele, ... - der einfachste Weg, wenn du Machine Learning lernen möchtest. Schritt für Schritt bringe ich dir maschinelles Lernen bei. In jedem Abschnitt lernst du ein neues Thema - zuerst die Idee / Intuition dahinter, und anschließend den Code sowohl in Python als auch in R.Machine Learning macht erst dann richtig Spaß, wenn man echte Daten auswertet. Deswegen analysierst du in diesem Kurs besonders viele Praxisbeispiele:Schätze den Wert von GebrauchtwagenSchreibe einen Spam-FilterDiagnostiziere BrustkrebsSchreibe ein Programm, was die Bedeutung von Adjektiven lerntLese Zahlen aus Bildern einAlle Codebeispiele werden dir beiden Programmiersprachen gezeigt - du kannst also wählen, ob du den Kurs in Python, R, oder in beiden Sprachen sehen möchtest!Nach dem Kurs kannst du Machine Learning auch auf eigene Daten anwenden und eigenständig fundierte Entscheidungen treffen:Du weißt, wann welche Modelle in Frage kommen könnten und wie du diese vergleichst. Du kannst analysieren, welche Spalten benötigt werden, ob zusätzliche Daten benötigt werden, und weißt, die die Daten vorab aufbereitet werden müssen. Dieser Kurs behandelt alle wichtigen Themen:RegressionKlassifizierungClusteringNatural Language ProcessingBonus: Deep Learning (nur für Python, weil die Tools hier sehr viel ausgereifter sind)Zu allen diesen Themen lernst du verschiedene Algorithmen kennen. Die Ideen dahinter werden einfach erklärt - keine trockenen, mathematischen Formeln, sondern anschauliche, grafische Erklärungen.Wir verwenden hierbei g
This program equips insurance professionals with the skills to leverage Generative AI tools to enhance efficiency and accuracy in underwriting and claims processing, improve customer service, and perform risk assessments.
Selamat datang di program pelatihan data science dan machine learning dengan Python!Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dari sudut terapan dengan memanfaatkan Python.Bagi rekan - rekan yang belum menguasai pemrograman Python, pelatihan juga memberikan konten pemrograman dasar untuk Python sehingga rekan - rekan dapat mengikuti pelatihan ini dengan baik. Bagi yang sudah bisa pemrograman Python, rekan - rekan dapat melanjutkan di topik berikutnya.Seluruh konten didalam pelatihan ini dilaksanan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihanPemrograman PythonPython Virtual EnvironmentPengolahan dan Analisa Data - Numpy dan PandasTopik Khusus - Numpy dan Pandas - DatabaseVisualisasi Data dengan memanfaatkan library Matplotlib, Seaborn dan BokehTopik Khusus Visualisasi Data Time SeriesDataset, Pra-Proses dan Pengurangan Dimensi Feature (Dimensionality Reduction)Permasalahan dan Penyelesaian Kasus Linear RegressionPermasalahan dan Penyelesaian Kasus Klasifikasi (Classification)Permasalahan dan Penyelesaian Kasus Kekelompokkan (Clustering)Hyperparameter Tuning Untuk Model Machine LearningEnsemble MethodsReinforcement LearningAutomated Machine Learning (AutoML)Kumpulan Studi KasusJika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada web ini sehingga rekan-rekan lainnya dapat mengetahui dan
3.997 / 5.000Aprender a programar en Python no siempre es fácil, especialmente si desea usarlo para la ciencia de datos. De hecho, hay muchas herramientas diferentes que deben aprenderse para poder usar correctamente Python para la ciencia de datos y el aprendizaje automático, y cada una de esas herramientas no siempre es fácil de aprender. Pero, este curso le dará todos los conceptos básicos que necesita sin importar para qué objetivo quiera usarlo, así que si: - Es estudiante y desea mejorar sus habilidades de programación y desea aprender nuevas utilidades sobre cómo usar Python - Necesidad de aprender los conceptos básicos de la ciencia de datos. - Debe comprender las herramientas básicas de ciencia de datos para mejorar su carrera. - Simplemente adquiera las habilidades para uso personal Entonces definitivamente te encantará este curso. No solo aprenderá todas las herramientas que se utilizan para la ciencia de datos, sino que también mejorará su conocimiento de Python y aprenderá a usar esas herramientas para poder visualizar sus proyectos. La estructura del curso Este curso está estructurado de manera que podrá aprender cada herramienta por separado y practicar programando en Python directamente con el uso de esas herramientas. De hecho, al principio aprenderá todas las matemáticas asociadas con la ciencia de datos. Esto significa que tendrá una introducción completa a la mayoría de las funciones y fórmulas estadísticas importantes que existen. También aprenderá a configurar y utilizar Jupyter, así como a escribir su código Python. Después, aprenderá las diferentes bibliotecas de Python que existen y cómo usarlas correctamente. Aquí aprenderás herramientas como NumPy o muchas otras.Finalmente, tendrá una introducción al aprendizaje automático y aprenderá cómo funciona un
A course designed for B2B marketers, combining strategy and hands-on tactics. It covers topics like generative content-driven demand creation and AI-driven editorial calendars.
An educational initiative funded by the EPSRC Impact Acceleration Account at Aston University. The project aims to foster an interdisciplinary approach to responsible AI in medical imaging.
Learn Fundamentals of Deep Learning
Data Science is an interdisciplinary field that leverages statistical analysis, data exploration, and machine learning techniques to derive knowledge and meaningful insights from data.Definition of Data Science:Data Science encompasses various processes, including data acquisition, thorough analysis, and informed decision-making.Data Science involves the identification and interpretation of data patterns to make predictive assessments.Through the application of Data Science, organizations can achieve:1. Improved decision-making processes, enabling the selection between alternatives (A or B) with greater confidence.2. Predictive analysis that anticipates future events or trends, aiding in proactive planning.3. Discovery of hidden patterns and valuable information within datasets, leading to actionable insights.Applications of Data Science:Data Science finds extensive application across diverse industries such as banking, consultancy, healthcare, and manufacturing.Examples of Data Science applications include:1. Optimizing route planning for shipping purposes.2. Anticipating potential delays in flights, ships, trains, etc., through predictive analysis.3. Crafting personalized promotional offers for customers.4. Determining the best time to deliver goods for maximum efficiency.5. Forecasting future revenue for a company.6. Analyzing the health benefits of specific training regimens.7. Predicting election outcomes.Data Science Integration in Business:Data Science can be seamlessly integrated into various facets of business operations where relevant data is available, including:1. Consumer goods industries for market analysis and consumer behavior prediction.2. Stock markets for financial analysis and forecasting.3. Industrial settings for process optimization and quality control.4. Political scenarios for opinion
Neste curso, você se aprofundará no universo da IA Generativa com LLMs (Large Language Models), explorando o potencial da combinação entre LangChain e Python. Você implementará soluções proprietárias (ChatGPT) e modelos open source modernos, como Llama e Phi. Por meio de projetos práticos e reais, você desenvolverá aplicações inovadoras, incluindo um assistente virtual personalizado e um chatbot que interage com documentos e vídeos. Vamos explorar técnicas avançadas como RAG e agentes, além de utilizar ferramentas como Streamlit para criar interfaces intuitivas. Você aprenderá a utilizar essas tecnologias gratuitamente no Google Colab e também a executar os projetos em ambiente local.Na introdução, você será apresentado à teoria dos Grandes Modelos de Linguagem (LLMs) e seus conceitos fundamentais. Além disso, será explorado o ecossistema da Hugging Face, que oferece soluções modernas de Processamento de Linguagem Natural (PLN). Você aprenderá a implementar LLMs utilizando tanto o pipeline da Hugging Face quanto a biblioteca LangChain, compreendendo as vantagens de cada abordagem.Na segunda parte, será abordado o domínio da LangChain. Você aprenderá a acessar modelos de código aberto, como o Llama da Meta e o Phi da Microsoft, além de LLMs proprietárias, como o ChatGPT da OpenAI. Será explicado como realizar a quantização de modelos, com o objetivo de melhorar a performance e a escalabilidade. Também serão apresentados os principais componentes do LangChain, como chains, templates e tools, e como utilizá-los para desenvolver soluções robustas em PLN. Técnicas de engenharia de prompt serão abordadas para ajudar a obter resultados mais precisos. O conceito de RAG (Retrieval-Augmented Generation) será explorado, incluindo o processo de armazenamento e recuperação de informações. Você aprenderá a implementar bancos de dados vetoriais (vector stores) e entenderá a importância dos embeddings e como utilizá-los de forma eficaz. Também será mostrado como usar RAG para inter
Learn Data Science with Python Certification
A 16-week online course on mastering data governance principles in AI environments. It covers designing ethical frameworks, automating compliance, and managing data quality using AI tools.
Build neural networks from scratch in code. Start with backprop, build up to transformers and GPT. Former Tesla AI Director teaches you everything.
Learn Can We Learn Generative AI Without Knowing Machine Learning And Deep Learning?
Готовы ли вы начать свой путь, чтобы стать Data Scientist?Специалист по анализу данных - одна из наиболее подходящих профессий для процветания в этом веке. Он цифровой, ориентированный на программирование и аналитический. Поэтому неудивительно, что спрос на специалистов по анализу данных на рынке труда растет.Однако предложение было очень ограниченным. Трудно получить навыки, необходимые для работы в качестве специалиста по данным.И как это сделать?Университеты не спешили создавать специализированные программы по науке о данных. (Не говоря уже о том, что существующие очень дороги и требуют много времени)Большинство онлайн-курсов сосредоточено на конкретной теме, и трудно понять, как навыки, которым они обучают, вписываются в общую картину.Этот всеобъемлющий курс станет вашим руководством к изучению того, как использовать возможности Python для анализа данных, создания красивых визуализаций и использования мощных алгоритмов машинного обучения! Курс регулярно пополняется новыми материалами!Этот курс подойдёт для всех - для начинающих без опыта программирования, для имеющих некоторый опыт программирования и для опытных разработчиков, стремящихся изучить Data Science!Вы научитесь программировать на Python, создавать удивительные визуализации данных и использовать машинное обучение с Python! Чему вы научитесь:Применять Python для Data ScienceИспользовать инструменты для работы в Data Science Научитесь использовать NumPy для числовых данныхНаучитесь использовать Pandas для анализа данныхНаучитесь использовать Matplotlib для визуализации данныхНаучитесь использовать Seaborn для визуализации данныхНаучитесь использовать встроенную визуализацию библиотеки PandasНаучитесь применять новые знания на практикеНаучитесь использовать библиотеки Machine LearningИ многое другое!Записывайтесь на курс и получите одну из самых востребованных профессий и супер
Would you like to learn how to detect if someone is wearing a Face Mask or not using Artificial Intelligence that can be deployed in bus stations, airports, or other public places?Would you like to build a Convolutional Neural Network model using Deep learning to detect Covid-19 Face Mask?If the answer to any of the above questions is "YES", then this course is for you.Enroll Now in this course and learn how to detect Face Mask on the static images as well as in the video streams using Tensorflow and OpenCV. As we know, COVID-19 has affected the whole world very badly. It has a huge impact on our everyday life, and this crisis is increasing day by day. In the near future, it seems difficult to eradicate this virus completely.To counter this virus, Face Masks have become an integral part of our lives. These Masks are capable of stopping the spread of this deadly virus, which will help to control the spread. As we have started moving forward in this ‘new normal’ world, the necessity of the face mask has increased. So here, we are going to build a model that will be able to classify whether the person is wearing a mask or not. This model can be used in crowded areas like Malls, Bus stands, and other public places.This is a hands-on Data Science guided project on Covid-19 Face Mask Detection using Deep Learning and Computer Vision concepts. We will build a Convolutional Neural Network classifier to classify people based on whether they are wearing masks or not and we will make use of OpenCV to detect human faces on the video streams. No unnecessary lectures. As our students like to say :"Short, sweet, to the point course"The same techniques can be used in :Skin cancer detectionNormal pneumonia detectionBrain
A 3-week online program focused on using AI to create original music from scratch. The course covers using generative AI platforms like Amper Music and AIVA to produce custom songs based on user inputs.
An interactive, hands-on course where you learn to build and use decision trees and random forests, two powerful supervised machine learning models. Includes projects to solidify your understanding.
A two-day, on-campus course for business leaders and directors of strategy, focusing on leveraging AI for strategic value and growth with case studies from leading companies and actionable frameworks.
A practical guide to data wrangling using Python and the pandas library. The article covers reading data, accessing columns and rows, handling missing values, and data normalization, which are crucial steps for preparing data for analysis and machine learning.
A course for experienced designers focusing on strengthening usability and accessibility skills. It teaches how to integrate universal design principles and advocate for inclusivity in design to create intuitive and accessible digital experiences.
Learn How AI Image Generators Work (Stable Diffusion / Dall-E) - Computerphile
Learn Generative AI in a Nutshell - how to survive and thrive in the age of AI
A project-based course that teaches how to utilize ChatGPT to create surveys, polls, and other artifacts for both primary and secondary market research. It also covers harnessing the power of ChatGPT for translating market research text and collaterals into multiple languages.
This course explores the ethical challenges and complexities of AI's role in mental health, covering topics like bias, misinformation, privacy, and patient safety. It delves into advancements in computing, social robotics, and NLP techniques used in mental health analysis. The course is designed for mental health professionals, policymakers, and tech leaders.
This career path equips you with the skills to manage data, get results, and drive decision-making. It includes modules on data wrangling and cleaning with Python.
A four-week online course for creative professionals to master AI tools like Midjourney, ChatGPT, and DALL-E for visual design. The course includes video lectures, hands-on projects, and peer group sessions.
Offers a wide range of courses and certifications specifically for technical writers who want to integrate AI into their work. The platform provides daily updates on AI tools and news and is suitable for all skill levels.
A webinar focused on why data literacy for AI is essential for fostering innovation and achieving organizational growth. It covers AI's impact across industries, understanding AI, data & AI literacy, and building an AI-ready workforce.
Learn The Essential Main Ideas of Neural Networks
Learn All Machine Learning algorithms explained in 17 min
This course dives into the ethical issues surrounding AI technologies, including the challenges posed by AI development and usage. It provides a structured understanding of ethical frameworks to guide AI implementation.
A comprehensive 7-course series that takes you from LLM business strategy to production deployment. You will learn to evaluate LLM opportunities, fine-tune models, and build production-ready applications using tools like Hugging Face and Python.
This certificate program from Cornell University explores the emerging field of precision nutrition, which tailors recommendations to individual genetic profiles, lifestyle, and environmental factors. The course delves into the applications, limitations, and ethical considerations of personalized nutrition.
This certificate program from Cornell University covers the fundamentals of machine learning. It includes a specific course on 'Debugging and Improving Machine Learning Models' where you will learn to identify causes of prediction error, understand the bias-variance trade-off, and use ensemble methods to improve model performance.
A specific course within Cornell's Machine Learning Certificate program that focuses on investigating the prediction accuracy of machine learning algorithms. You will learn to recognize high bias and variance to reduce prediction errors and implement techniques like bagging and boosting to create more reliable models.
Learn How To Learn Math for Machine Learning FAST (Even With Zero Math Background)
This course teaches you how to create chatbots without writing any code. You will learn to plan, implement, test, and deploy chatbots using IBM Watson's Assistant that are designed to be effective and user-friendly.
This IBM course explores transformers and key model frameworks like Hugging Face and PyTorch. It covers optimizing LLMs and advances to fine-tuning generative AI models using techniques like PEFT, LoRA, and QLoRA.
This specialization teaches you to apply AI techniques to scientific research. You will learn to use Python, scikit-learn, TensorFlow, and Keras to work with scientific data, build and evaluate machine learning models like neural networks and random forests, and complete a capstone project on drug discovery.
This course explores the power of Artificial Intelligence across diverse fields including manufacturing. It bridges the gap between theory and practical applications, providing hands-on experience with AI algorithms for tasks like fault diagnosis and process optimization.
This course equips healthcare professionals and enthusiasts with practical AI skills to improve patient care and streamline operations. It covers AI fundamentals, machine learning, natural language processing, predictive analytics, and ethical healthcare practices. Learners will explore the application of AI in medical imaging, diagnostics, treatment planning, and personalized medicine while understanding compliance and regulatory standards.
This course covers key Industry 4.0 technologies including AI, machine learning, and big data analytics, and their applications in creating smart factories and improving production processes.
This course covers fundamental mathematical concepts for machine learning, including derivatives, integrals, and optimization techniques like gradient descent.
Learn Transformer Neural Networks - EXPLAINED! (Attention is all you need)
A focused course on logistic regression and other supervised machine learning techniques using Python.
This course equips account managers with AI strategies to enhance client management by personalizing communication, automating workflows, and using predictive analytics to anticipate client needs.
This specialization teaches how to leverage Generative AI in the mobile app development lifecycle. You will learn to create code, prototypes, and optimized programs, debug and enhance code using GenAI, and tackle ethical challenges. The courses cover using AI tools like Vertex AI, Dialogflow, and Apple Intelligence.
Course materials for a graduate-level class on optimization for machine learning, including lecture notes and problem sets.
Master deep learning using the PyTorch framework. Build and train neural networks for computer vision and NLP applications.
This course equips supply chain and operations managers with hands-on experience in AI tools to simplify complex data and make smarter decisions to boost efficiency and reduce risks.
Learn What is YOLO algorithm? | Deep Learning Tutorial 31 (Tensorflow, Keras & Python)
Learn Visual Guide to Transformer Neural Networks - (Episode 1) Position Embeddings
This course covers the entire process of building, evaluating, and operationalizing machine learning models. You will learn to assess model performance using key metrics and cross-validation techniques and explore methods for improving model efficiency.
A course that focuses specifically on classification techniques within supervised learning, offered by IBM.
This course explores new metrics and best practices to monitor your LLM systems and ensure safety and quality. You will learn to identify hallucinations, detect jailbreaks using sentiment analysis, identify data leakage, and build your own monitoring system.
A course aimed at providing the necessary tools and techniques to succeed in the changing field of financial fraud prevention. It features insights from an instructor with over 25 years of experience in Fraud Detection and AI/ML.
Learn Precision Agriculture with AI and Machine Learning
Learn Transformer Neural Networks Derived from Scratch
A three-course certificate program that provides the knowledge to use AI as a force multiplier in cybersecurity, focusing on spotting patterns and adapting in real-time.
This course explores the application of Transformers in video understanding, with a focus on action recognition and instance segmentation, and covers recent developments in large-scale pre-training and multimodal learning.
This course explores supervised learning techniques for marketing applications. The curriculum covers customer behavior analysis, product recommendation systems, and customer lifetime value prediction.
This program covers a wide range of topics including AI and machine learning fundamentals for renewable energy, energy generation forecasting, data analytics for energy efficiency, predictive maintenance, and smart grid integration.
Learn Neural Networks and Deep Learning: Crash Course AI #3
Learn Natural Language Processing: Crash Course AI #7
This course, part of a Machine Learning for Supply Chain Fundamentals specialization, explores all aspects of time series for demand prediction. It covers basic concepts like stationarity, trend, and seasonality, and then moves to autoregressive models and a final project on predicting demand using ARIMA in Python.
This course is designed to empower developers, this comprehensive guide provides a practical approach to integrating LangcChain with OpenAI and effectively using Large Language Models (LLMs) in Python.In the course's initial phase, you'll gain a robust understanding of what Langchain is, its functionalities and components, and how it synergizes with data sources and LLMs. We'll briefly dive into understanding LLMs, their architecture, training process, and various applications. We'll set up your environment with a hands-on installation guide and a 'Hello World' example using Google Colab.Subsequently, we'll explore the LangChain Models, covering different types such as LLMs, Chat Models, and Embeddings. We'll guide you through loading the OpenAI Chat Model, connecting LangChain to Huggingface Hub models, and leveraging OpenAI's Text Embeddings.The course advances to the essential aspect of Prompting & Parsing in LangChain, focusing on best practices, delimiters, structured formats, and effective use of examples and Chain of Though Reasoning (CoT).The following sections focus on the concepts of Memory, Chaining, and Indexes in LangChain, enabling you to handle complex interactions with ease. We will study how you can adjust the memory of a chatbot, the significance of Chaining, and the utility of Document Loaders & Vector Stores.Finally, you'll delve into the practical implementation of LangChain Agents, with a demonstration of a simple agent and a walkthrough of building an Arxiv Summarizer Agent.By the end of this course, you'll have become proficient in using LangChain with OpenAI LLMs in Python, marking a significant leap in your developer journey. Ready to power up your LLM applications? Join us in this comprehensive course!
This course offers a contemporary, tech-focused method for fighting fraud in various industries. It provides professionals with the necessary tools and frameworks to use AI effectively in risk management and fraud prevention.
This training course covers core AI technologies and their relevance in logistics and transport management, the role of AI in forecasting, warehouse management, and delivery optimization.
Learn Diffusion Models for AI Image Generation
A two-part series that guides public sector professionals through building responsible AI initiatives, focusing on managing risks and developing AI skills to transform government operations.
This course provides an in-depth look at the theory and methods of optimization.
An intermediate-level course for data scientists and ML engineers on building models with limited labeled data. It covers zero-shot and few-shot learning techniques, applying pre-trained models, semantic embeddings, and transfer learning.
This course provides foundational principles of accessibility and inclusive design. It covers major disability types, assistive technologies, legal aspects, and the principles of universal design and accessible content creation.
This course teaches how to harness the power of AI to enhance user research and design methodologies. Participants will explore AI tools and techniques that can streamline data gathering, analysis, and interpretation in user research and enhance ideation and prototyping.
A free, self-paced course that introduces top AI tools like Wisecut, Veed.io, and Synthesia to create high-impact videos. You'll learn to automate edits, enhance quality, and boost engagement.
Learn to use GPT, DALL-E, Whisper and other OpenAI models for real applications.
Learn Advanced Writing with Grammarly AI
This course offers a comprehensive overview of WebAssembly, from its fundamental principles to its applications in cloud computing. It includes hands-on activities and real-world examples to solidify understanding of WebAssembly's core concepts.
Master TensorFlow 2 and Keras. ANNs, CNNs, RNNs, GANs, deployment.
A free course with over 50 theoretical lessons and 10 practical projects, teaching how to train, fine-tune, and deploy LLMs into AI products. It covers SFT, RLHF, LoRA, and custom model training.
This course reviews the mechanisms behind anthropogenic climate change and its impact on global temperatures and weather patterns. It includes two case studies: one using time series analysis for wind power forecasting and another using computer vision for biodiversity monitoring, demonstrating how AI techniques can help mitigate and adapt to climate change.
This course explores the ethics and responsible use of generative AI tools. Learners will engage with these tools with a focus on intentionality, sustainability, and responsibility, and learn to evaluate them using the SIFT process.
This course teaches how to use applied machine learning and text-mining techniques to analyze free-text data. You will learn to identify named entities, tag them with appropriate classifications, and develop multiple approaches from regular expressions to neural network models for extraction.
This course explores the application of AI in revolutionizing energy systems and advancing healthcare. In the energy sector, it covers AI-driven techniques like predictive maintenance, demand forecasting, and energy storage optimization.
While not a technical course, this highly-rated course by Andrew Ng explains the business aspects of AI and touches upon the importance of data and statistical thinking in building AI systems.
A comprehensive system for capturing, processing, enhancing, and monetizing 3D reconstructions using open-source tools and Python automation. Includes modules on 3D Python, Point Cloud Processor, and 3D Vision.
A specialized course exploring the adoption of AI in medical imaging, covering both technical aspects and ethical challenges. The program is particularly relevant for radiologists and imaging specialists and focuses on AI applications in image analysis, ethical considerations in AI imaging, and clinical validation methodologies.
This program is for finance professionals and operations managers, teaching how to apply machine learning, natural language processing, and predictive analytics to identify cost inefficiencies and develop mitigation strategies.
This online diploma course provides an in-depth understanding of forecasting fashion trends and making predictions for collections. It covers macro and micro trends, the influence of digital platforms and AI, and how to use quantitative data for trend analysis.
This course from Kennesaw State University is designed to help learners use generative AI to enhance their grant writing skills. It covers prompt engineering and ethical AI use to streamline the grant writing process, from creating solicitation letters to structuring detailed proposals.
This certification course provides policymakers, analysts, and public sector professionals with the knowledge to use AI responsibly. It covers governance frameworks, ethics, and real-world applications from predictive analytics to public safety.
A detailed article that provides a crash course in the terminology and processes used in the field of multi-layer perceptron artificial neural networks. It covers neurons, weights, activations, and how networks of neurons are trained.
A comprehensive guide in the form of a book that teaches how to use deep learning methods for time series forecasting with Python.
Learn DeepLearning.AIUnsupervised Learning, Recommenders, Reinforcement LearningCourse
Learn GoogleDiscover the Art of PromptingCourse
A 7-day crash course designed to get developers started with function optimization in Python. It covers topics like grid search, SciPy optimization algorithms, BFGS, hill-climbing, simulated annealing, and gradient descent.
An live course focused on leveraging AI for in-depth customer research to inform user experience and product strategy.
This course provides insights into AI-driven strategies to detect, analyze, and counter disinformation. It covers techniques to identify misinformation patterns, assess threats, and implement effective countermeasures.
This course teaches you how to analyze data using Python and popular libraries like Pandas and NumPy. You'll learn about data preparation, wrangling, and exploratory data analysis.
This course emphasizes four main ideas that are central to understanding how AI can impact government operations, including the different types of AI, how it can automate and augment work, and overcoming challenges to scaling AI in the public sector.
The first course in this specialization is an excellent entry point for practical computer vision. You will learn to build and deploy models in the browser using TensorFlow.js, including creating a real-time object detector that runs on a webcam feed.
A comprehensive course on machine learning from NPTEL that includes modules on Support Vector Machines and Kernel Methods. The course is available for free online.
This course explores how AI can transform businesses, the role of explainable AI, responsible AI governance strategies, and the impact of AI on organizational structure.
This course provides a comprehensive overview of knowledge graphs, their underlying technologies, and their significance in today's digital world. You will learn what is necessary to design, implement, and apply knowledge graphs, with a focus on basic semantic technologies including RDF, OWL, and SPARQL.
Learn Learn Machine Learning Like a GENIUS and Not Waste Time
Learn MLOps with Weights & Biases
Learn Functions, Tools and Agents with LangChain
A highly respected and comprehensive online textbook on time series forecasting that covers a wide range of topics with examples in R.
A one-week program focused on a deep understanding of AI optimization techniques to improve the efficiency of machine learning algorithms and enhance overall performance. It covers high-level AI model optimization strategies and state-of-the-art methods for performance improvement.
A comprehensive program designed to equip professionals with the skills to harness AI and machine learning for strategic decision-making. The course covers AI-driven decision-making strategies, data-driven decision-making with AI, and the application of predictive analytics in real-world business scenarios.
This course explores how AI-enabled technologies are revolutionizing policing, criminal investigations, and judicial proceedings, while also addressing ethical concerns and regulatory frameworks.
Learn But what is a neural network? | Deep learning chapter 1
A week-long training program that provides in-depth knowledge of sentiment analysis techniques driven by AI, with a focus on NLP, machine learning, and deep learning.
An online training course designed for patent agents and attorneys to understand the intersection of AI technologies and patent law, covering legal complexities and the patentability of AI technologies.
This course covers the fundamentals of product discovery, how to leverage AI at each stage, scaling strategy, and the importance of product discovery for business success.
Learn Harvard CS50's Introduction to Artificial Intelligence
This course introduces Bayesian approaches to time series analysis, covering models like Autoregressive (AR) and Dynamic Linear Models (DLMs).
This course in the DeepLearning.AI specialization covers the essential calculus concepts needed to understand and build machine learning models.
This course focuses on the methods used to measure causal effects in the social sciences, a key area for practitioners.
Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. TensorFlow, an open source software library, is extensively used for numerical computation using data flow graphs.The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. So if you’re looking forward to acquiring knowledge on machine learning and deep learning with this powerful TensorFlow library, then go for this Learning Path. Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Setting up TensorFlow for actual industrial use, including high-performance setup aspects like multi-GPU support Embedded with solid projects and examples to teach you how to implement TensorFlow in production Empower you to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage Let's take a look at your learning journey. You will start by exploring unique features of the library such as data flow graphs, training, visualization of performance with TensorBoard – all within an example-rich context using problems from multiple industries. The focus is towards introducing new concepts through problems which are coded and solved over the course of each video. You will then learn how to implement TensorFlow in production. Each project in this Learning Path provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Finally, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more,
Bienvenido a este curso 100% práctico y aplicado en el que podrás aprender de forma intuitiva, guiada y paso-a-paso el despliegue de modelos Deep Learning para ambientes de Desarrollo y principalmente para Producción en escenarios de alto desempeño usando la librería TensorFlow 2.0 y la creación de servicios REST.Estructura temática:¿Por qué desplegar modelos Deep Learning?Despliegue en Desarrollo vs ProducciónServidores de despliegue en la Nube: Google Cloud Platform (GCP) y CentOSDespliegue de modelos en la nube como Servicio Web REST desde cero (APIs)Gestor de contenedores Docker para despliegues en Producción (Docker Swarm, TensorFlow Serving) Implementación de llamadas al Servicio Web desde ceroConsideraciones técnicas para el despliegue de modelos Deep LearningDevOps y Machine Learning / MLOps | IAOps | XXOpsInteroperabilidad de modelos: ONNX.Despliegue Customizado vs Plataformas100% práctico:El curso prioriza el desarrollo de algoritmos en sesiones de laboratorio y actividades de programación 100% hands-on con los que podrás reproducir cada una de las líneas de código con explicaciones muy bien detalladas, sin descuidar los fundamentos teóricos de cada uno de los conceptos descritos.Herramientas:Todas las herramientas necesarias para el curso se podrán configurar directamente en la nube de Google; por tanto, no será necesario invertir tiempo en instalaciones de herramienta de forma local.El curso se desarrolla con las herramientas más populares y de alta madurez del ecosistema de Python 3.0 como:TensorFlow 2.0TensorFlow ServingFlask
A collection of AI programs designed to provide a strategic understanding of AI's impact on business. Courses cover topics like AI ethics, AI marketing, AI strategy for business leaders, and leveraging multi-sided platforms with AI.
An in-depth, hands-on course on applying Data Science, Machine Learning, and GIS to Real Estate. It covers Python, Pandas, and Scikit-Learn for analyzing and forecasting property globally.
Customized AI training program for businesses to enhance skills in areas like maximizing pricing strategies, optimizing promotional activities, and enhancing mix management.
A free online course covering generative AI, global AI laws, AI risk management, and AI governance frameworks. The course consists of 8 modules and a final certification exam.
This course teaches how to use generative AI tools like ChatGPT strategically to create compelling, funder-ready grant proposals. It provides AI prompt templates to streamline the workflow and enhance grant writing skills.
A collection of talks from a workshop on optimization for large-scale machine learning, featuring leading researchers in the field.
An online certificate program that teaches the use of AI tools to improve mental health outcomes for clients. It provides a comprehensive understanding of AI principles and their application in assessing and addressing mental health challenges, including ethical considerations, within a social work context.
This in-person workshop focuses on leveraging AI to customize proposals and make them more appealing to funders. It covers the essential components of successful grant systems, from strategic alignment to data-driven storytelling.
A program for professionals to learn to harness generative AI, hyper-personalization, and predictive analytics to optimize customer engagement and drive growth in marketing.
A course that demonstrates a workflow starting with AI-generated music and then producing, arranging, and modifying it to infuse human creativity. It focuses on taking AI-generated ideas as a starting point and building upon them.
This program focuses on AI-driven solutions, smart grid optimization, and data analytics for sustainable energy management, designed for engineers, data scientists, and energy professionals.
Equips professionals with skills to optimize energy systems using AI. The curriculum focuses on predictive analytics, energy forecasting, and smart grid optimization through hands-on projects.
This program equips public sector leaders with skills to use AI for transformative governance, focusing on AI-driven decision-making, ethical frameworks, and innovative strategies to enhance public services.
This program is designed for environmental scientists, tech innovators, and policy makers, combining machine learning, climate modeling, and renewable energy strategies. It offers hands-on experience in applying AI to reduce carbon footprints and optimize resource management to become a leader in climate innovation.
An undergraduate certificate program that blends AI-driven fashion insights with machine learning techniques to revolutionize styling trends and personalization. The course teaches how to analyze fashion data, predict trends, and create personalized styling solutions. It is designed for students, designers, and marketers looking to gain cutting-edge skills in the digital fashion industry.
Learn IBM Watson Machine Learning Professional Certificate
Teaches the fundamentals of penetration testing for AI/LLM-based applications through self-paced video instruction and guided hands-on labs. Students will learn to detect and exploit common AI vulnerabilities such as prompt injection and sensitive information disclosure.
A 2-hour on-demand course that focuses on the unique legal and practical issues associated with AI systems. It provides guidance on drafting and negotiating new contractual terms for these technologies.
A series of four courses covering essential topics in technology contracting, including AI contracts. The master class is available as a bundle or individual courses and includes live webinars with Q&A sessions.
Learn Generative AI with Large Language Models
This course teaches students how to use AI models like GPT-4, Gemini, Cohere, and Grok for technical writing. It covers generating outlines, summaries, proposal structures, and information architecture, as well as using AI for SEO, plagiarism, and content creation. The course aims to provide a strong portfolio in AI writing.
A comprehensive program for senior professionals and executives covering data science, AI for decision-makers, AI-powered marketing, project management for AI, blockchain, and tokenomics. It aims to teach these technologies without jargon in a cost-effective manner.
This course delves into natural language processing (NLP), teaching you how to build models for tasks like sentiment analysis and text classification. You'll learn about logistic regression and naive Bayes, and how to represent text as vectors.
This comprehensive course is designed to provide learners with the knowledge and skills required to conduct high-quality systematic literature reviews using AI tools, guiding them through every stage of the process.
A collection of articles and tutorials on various optimization algorithms used in deep learning, providing both theoretical explanations and practical code examples.
Scopri l'avanguardia del linguaggio e della tecnologia con il mio Percorso Formativo Esclusivo su ChatGPT e Machine Learning!Siamo sulla soglia di una nuova era in cui l'intelligenza artificiale e il machine learning stanno ridefinendo il modo in cui interagiamo con la tecnologia. Per esempio, potrai usare ChatGPT per automatizzare la risposta alle domande dei tuoi clienti o per creare contenuti di blog in modo automatico. Sei pronto a essere al centro di questa rivoluzione? Allora, il mio corso è l'investimento perfetto per il tuo futuro!Questo corso è stato progettato per fornire una comprensione approfondita dei concetti chiave di Machine Learning e delle tecniche di Prompt Engineering, utilizzando come base le versioni disponibili al momento della creazione del corso.Sebbene la piattaforma possa subire aggiornamenti o modifiche nel tempo, i concetti e le strategie insegnati in questo corso sono progettati per essere universalmente applicabili, anche alle versioni più recenti e a quelle future. Questo ti permetterà di acquisire competenze durature e facilmente adattabili al progresso delle tecnologie IA.Immergiti nel cuore del machine learning, delle reti neurali e dei modelli di linguaggio con questo percorso formativo. Scoprirai ChatGPT, un software che utilizza uno dei modelli di linguaggio più avanzati al mondo, e imparerai a utilizzarlo per creare contenuti coinvolgenti, ottimizzare le tue tecniche di marketing e portare il tuo lavoro o i tuoi studi al livello successivo.Con il nostro corso, avrai l'opportunità di esplorare: I fondamenti del Machine Learning e le diverse tipologie di apprendimento; La struttura e il funzionamento delle reti neurali; L'architettura e l'apprendimento di ChatGPT; Le tecniche di Prompt Engineering per generare contenuti coinvolgenti; L'importan
Learn AI, Machine Learning, Deep Learning and Generative AI Explained
This online graduate certificate program provides knowledge and skills in fundamental theories, concepts, and deliverables in learning analytics, including collecting, storing, and managing educational data, basic statistics, programming in analytics software, and data visualization.
A 5-week live, interactive course designed for educators on AI in education. It provides hands-on training on AI concepts, applications, and tools with government-approved certification.
Learn Computer Vision Masterclass with OpenCV and Deep Learning
A self-guided course for health and fitness coaches on utilizing AI tools like ChatGPT and DALL-E 3 to create personalized client resources, marketing content, and enhance coaching methodologies. The course offers practical features like simulated client interactions and feedback analysis.
Learn Illustrated Guide to Transformers Neural Network: A step by step explanation
Learn Explaining Prompting Techniques In 12 Minutes – Stable Diffusion Tutorial (Automatic1111)
This course is for UX professionals or career changers looking to learn about voice user interface design. It offers a chance to build a portfolio by working on a project from use case selection to publishing a skill.
This course teaches you how to integrate AI into your user research practice responsibly and effectively. You'll learn to save time and work efficiently while keeping empathy and critical thinking at the center of your work. It covers the role of AI in research, prompting for research, AI for research planning, facilitation, analysis, and synthesis, and evaluating AI tools.
A tutorial on optimization algorithms for machine learning, focusing on gradient-based and stochastic methods.
A certification that teaches the fundamentals of AI and machine learning and their application in real estate for market research, strategy, risk analysis, and data visualization.
A collection of courses by Weaviate to master their vector database. It includes modules on text data, multimodal data, vector indexes, and more. The courses are project-based and designed to provide core knowledge and essential skills for building with Weaviate.
A workshop focused on integrating Generative AI technologies with WebRTC. The course covers the evolution of WebRTC, the challenges of real-time AI, and analyzes how different vendors are approaching these challenges.
An industry-recognized certification program that provides hands-on experience and covers the foundations of AI in design, AI tools and technologies, content creation, user research, and visual design with AI.
A free, 2-hour, hands-on course designed to help educators get started with ChatGPT to save time, engage students, and implement AI responsibly.
This online course teaches how to build a data-driven L&D function with learning analytics to measure the impact and effectiveness of training programs and design cost-effective learning interventions with high ROIs.
Artificial Intelligence is the next digital frontier, with profound implications for business and society. The global AI market size is projected to reach $202.57 billion by 2026, according to Fortune Business Insights.This Data Science & Machine Learning (ML) course is not only ‘Hands-On’ practical based but also includes several use cases so that students can understand actual Industrial requirements, and work culture. These are the requirements to develop any high level application in AI. In this course several Machine Learning (ML) projects are included.1) Project - Customer Segmentation Using K Means Clustering2) Project - Fake News Detection using Machine Learning (Python)3) Project COVID-19: Coronavirus Infection Probability using Machine Learning4) Project - Image compression using K-means clustering | Color Quantization using K-MeansThis course include topics ---What is Data Science Describe Artificial Intelligence and Machine Learning and Deep Learning Concept of Machine Learning - Supervised Machine Learning , Unsupervised Machine Learning and Reinforcement LearningPython for Data Analysis- Numpy Working envirnment-Google ColabAnaconda Installation Jupyter Notebook Data analysis-PandasMatplotlib What is Supervised Machine LearningRegressionClassification Multilinear Regression Use Case- Boston Housing Price Prediction Save Model Logistic Regression on Iris Flower Dataset Naive Bayes Classifier on Wine Dataset Naive Bayes Classifier for Text Classification Decision TreeK-Nearest Neighbor(KNN) Algorithm Support Vector Machine Algor
Learn Transformers and Self-Attention (DL 19)
Learn Transformers Explained - How transformers work
A comprehensive certification program designed for HR professionals looking to leverage AI. The course covers the practical applications of AI in HR, including enhancing recruitment with AI-powered assessment tools. It provides a strategic overview of how to implement AI in HR processes effectively and ethically.
This article explores the concept of holistic data quality management in the context of AI. It emphasizes the importance of a continuous improvement process involving people, processes, and technology to ensure clean, accurate, and reliable data for successful AI initiatives. The article also discusses the role of AI in automating data profiling and quality monitoring.
This course delves into the role of Artificial Intelligence in optimizing performance, strategy, and decision-making within the sports industry. Participants will gain a comprehensive understanding of key AI and Machine Learning concepts, tailored specifically to the needs and challenges of sports professionals.
This course explores the best practices for responsible AI implementation in medicine. It is part of a specialized set of programs designed to train healthcare professionals to effectively use AI-driven tools, covering topics like bias in AI models, explainability, and the importance of human oversight.
This course discusses the psychological impact of AI, its implications on health, and its clinical application for mental health professionals. It reviews the progression of machine learning, ethical dilemmas, and current research into AI's use in mental health service delivery.
Learn Deep Learning: Getting Started
Learn Machine Learning and AI with Python Web Apps
Master RL algorithms from Q-learning to PPO. Build agents that learn from interaction and make optimal decisions.
This online course offers practical strategies for using AI in research, including how it can help in reading and understanding research faster, searching for scientific insights, and taking better notes. It also covers ethical challenges.
This course explores how artificial intelligence is revolutionizing the supply chain industry, enhancing efficiency, accuracy, and decision-making. It delves into AI-driven solutions such as demand forecasting, warehouse automation, and route optimization.
Learn TensorFlow 2.0 and Keras for deep learning. Build neural networks for computer vision, NLP, and time series prediction.
A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina).Também dentro do contexto da Aprendizagem de Máquina existe a área de Aprendizagem por Reforço, que é um tipo de aprendizagem usado em sistemas multi-agente no qual os agentes devem interagir no ambiente e aprenderem por conta própria, ganhando recompensas positivas quando executam ações corretas e recompensas negativas quando executam ações que não levem para o objetivo. O interessante dessa técnica é que a inteligência artificial aprende sem nenhum conhecimento prévio, adaptando-se ao ambiente e encontrando as soluções sozinho!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre a construção de um carro autônomo virtual utilizando aprendizagem por reforço! Vamos trabalhar com técnicas modernas de Deep Learning com a biblioteca PyTorch e a linguagem Python! Ao final você terá todas as ferramentas necessárias para solucionar outros tipos de problemas com aprendizagem por reforço. O conteúdo do curso está dividido em três partes:Teoria sobre aprendizagem por reforço com o algoritmo Q-LearningTeoria da aprendizagem por reforço
This course offers a comprehensive overview of how AI technologies are reshaping transportation industries on land, sea, and air, covering autonomous vehicles, public transit optimization, and more.
This Master's program is one of the first in the world to apply data analytics and forecasting in the context of fashion business. It combines creativity with analytics, teaching students how to use data-driven technologies to make informed decisions, model demand, and revolutionize traditional business models in the fashion industry. The course is delivered online and is supported by Stylumia, a global trend forecasting company.
This short online course equips students with digital tools to streamline fashion design. It explores AI-powered design ideation, sketch-to-photo technology, and 3D modeling software, enabling students to bring concepts to life before producing physical samples.
A specialized course focusing on tree-based methods and ensembles, covering decision trees, bagging, random forests, and gradient boosting machines.
More and more evidence has demonstrated that graph representation learning especially graph neural networks (GNNs) has tremendously facilitated computational tasks on graphs including both node-focused and graph-focused tasks. The revolutionary advances brought by GNNs have also immensely contributed to the depth and breadth of the adoption of graph representation learning in real-world applications. For the classical application domains of graph representation learning such as recommender systems and social network analysis, GNNs result in state-of-the-art performance and bring them into new frontiers. Meanwhile, new application domains of GNNs have been continuously emerging such as combinational optimization, physics, and healthcare. These wide applications of GNNs enable diverse contributions and perspectives from disparate disciplines and make this research field truly interdisciplinary.In this course, I will start by talking about basic graph data representation and concepts like node data, edge types, adjacency matrix and Laplacian matrix etc. Next, we will talk about broad kinds of graph learning tasks and discuss basic operations needed in a GNN: filtering and pooling. Further, we will discuss details of different types of graph filtering (i.e., neighborhood aggregation) methods. These include graph convolutional networks, graph attention networks, confidence GCNs, Syntactic GCNs and the general message passing neural network framework. Next, we will talk about three main types of graph pooling methods: Topology based pooling, Global pooling and Hierarchical pooling. Within each of these three types of graph pooling methods, we will discuss popular methods. For example, in topology pooling we will talk about Normalized Cut and Graclus mainly. In Global pooling, we will talk about Set2Set and SortPool. In Hierarchical pooling, we will talk about diffPool, gPool and SAGPool. Next, we will talk about three unsupervised graph neural network architectures: Graph
A practical, hands-on course that helps technical writers integrate AI, machine learning, and prompt engineering into their work. The curriculum includes foundational AI concepts, prompt engineering with tools like ChatGPT, and the automation of writing tasks.
This program blends ICF-accredited coaching mastery with digital fluency, empowering coaches to lead with confidence in a world where human insight and AI innovation go hand in hand.
This program is for professionals aiming to streamline and optimize business processes using AI-driven solutions. It focuses on designing and deploying AI-powered process optimization solutions using no-code tools and Lean Six Sigma principles.
Part of a specialization, this course covers essential cybersecurity concepts for forensics, including anomaly detection.
A hands-on project that teaches you how to perform data analysis and create visualizations directly within Google Sheets.
This course equips you with the skills to optimize data workflows, automate analysis, and generate actionable insights using AI. It covers automating ETL processes and generating synthetic data with tools like ChatGPT-4 and MOSTLY AI.
This course teaches how to integrate AI tools into your music production workflow to create professional-quality tracks. It covers AI-assisted songwriting, AI-generated instrument performances, and AI-powered mixing techniques.
Learn Google Cloud Machine Learning Complete Course
Part of the Johns Hopkins Data Science Specialization, this course covers the essential steps of obtaining and cleaning data, a critical prerequisite for any statistical analysis.
This video course introduces state-of-the-art first and second-order stochastic gradient methods for solving large-scale optimization problems and reviews their theoretical background on convergence rate analysis.
A series of summer courses offered by Harvard's CAUSALab, providing in-depth training on causal inference.
A self-study course for professionals to integrate AI technologies into fraud investigation practices.
An in-person course that introduces a general-purpose causal inference framework for experimental and non-experimental data, with a focus on health interventions.
This article argues that data literacy is a crucial prerequisite for AI literacy. It emphasizes the importance of critically interrogating data, whether qualitative or quantitative, to promote the ethical and equitable usage of AI and to understand and mitigate biases embedded in AI models.
An ISTC-accredited course that provides a framework for using generative AI tools like ChatGPT in various stages of a technical writing project. It aims to help technical communicators understand how to leverage AI to become more efficient and effective.
This webinar explains how facial recognition systems work, their benefits and risks, and common commercial applications. It also discusses social, ethical, and regulatory considerations for the use of these systems in research, including potential harms and privacy considerations.
This course covers the principles required to develop scalable machine learning pipelines, including a section on Recommendation Systems at Scale which discusses graph-networks, link analysis, collaborative filtering, and challenges of sparsity and scalability.
A webinar that explains how AI can be used to increase profits and reduce the risks associated with user-generated content. It covers scaling trust and safety programs, supporting human moderators, and addressing regional and cultural differences in content moderation.
Learn Data Engineer Path
Learn AI and Machine Learning Bootcamp
Part of a subscription, this course focuses on using AI tools for efficient prior art analysis and improving the quality of analysis.
A program for experienced professionals in non-technical roles. The curriculum includes 'Data literacy for AI-enabled innovation' to build essential data capabilities needed to drive successful AI initiatives.
This article emphasizes that data literacy is a critical prerequisite for successful AI implementation. It explains how a strong foundation in data literacy enables employees to source, clean, and analyze data for AI models, interpret their outputs, and ensure ethical and regulatory compliance.
Microsoft's AI School provides a variety of learning paths and resources for developers and data scientists. It covers a wide range of AI topics, including machine learning, conversational AI, and AI services on Azure.
Learn PyTorch for deep learning. CNNs, RNNs, GANs, transfer learning.
Hi this is Abhilash Nelson and I am thrilled to introduce you to my new course Deep Learning and Neural Networks using Python: For DummiesThe world has been revolving much around the terms "Machine Learning" and "Deep Learning" recently. With or without our knowledge every day we are using these technologies. Ranging from google suggestions, translations, ads, movie recommendations, friend suggestions, sales and customer experience so on and so forth. There are tons of other applications too. No wonder why "Deep Learning" and "Machine Learning along with Data Science" are the most sought after talent in the technology world now a days.But the problem is that, when you think about learning these technologies, a misconception that lots of maths, statistics, complex algorithms and formulas needs to be studied prior to that. Its just like someone tries to make you believe that, you should learn the working of an Internal Combustion engine before you learn how to drive a car. The fact is that, to drive a car, we just only need to know how to use the user friendly control pedals extending from engine like clutch, brake, accelerator, steering wheel etc. And with a bit of experience, you can easily drive a car. The basic know how about the internal working of the engine is of course an added advantage while driving a car, but its not mandatory. Just like that, in our deep learning course, we have a perfect balance between learning the basic concepts along the implementation of the built in Deep Learning Classes and functions from the Keras Library using the Python Programming Language. These classes, functions and APIs are just like the control pedals from the car engine, which we can use easily to build an efficient deep learning model.Lets now see how this course is organized and an overview about the list of topics included.We will be starting with few theory sessions in which we will see an overview about the Deep Learning an
A foundational lecture on the gradient descent algorithm within the context of the highly-rated Machine Learning course.
Learn AI-Enhanced Photography: From Capture to Creation
Learn AI-Powered Graphic Design
This course provides essential knowledge on stream processing and analysis using Apache Spark. Learners will gain an understanding of stream processing fundamentals and develop applications with the Spark Structured Streaming API.
Build a framework for thinking strategically about data and AI to improve organizational communication and create effective business strategies. This program will help you identify and avoid common pitfalls such as ethical concerns and unintended consequences.
This career path equips you with the R skills needed for a data analyst role, with a strong emphasis on data cleaning and manipulation using the Tidyverse.
A one-day immersive workshop led by Andrew Jones, the creator of data contracts. The course delves into the transformative world of Data Contracts and how to use them to implement a Data Mesh. It is tailored for software, platform, and data engineers looking for practical guidance on implementing data contracts and data mesh in their organizations.
Learn AI Automation Mastery: Complete Business Transformation
An intermediate-level course that builds on exploratory data analysis to lay the foundation for predictive modeling. It covers merging data, handling missing data, and special techniques for textual, audio, and image data.
This University of Michigan course explores the ethical considerations in data science, including fairness, accountability, and transparency, which are deeply connected to statistical concepts of bias and variance.
This course covers the mathematical foundations of optimization and its applications in data science.
This cheat sheet from Dummies.com provides a quick overview of the key concepts in statistics. It covers topics such as descriptive statistics, probability, and inference. The cheat sheet is a great resource for a quick review or to refresh your memory.
EC-Council provides free AI in Cybersecurity courses with their major certifications, covering topics like ChatGPT for ethical hacking and threat intelligence.
An intermediate-level course focusing on converting native code from languages like C/C++ and Rust into WebAssembly. It covers compilation techniques, VM infrastructure, and optimizing for performance. The course includes hands-on learning with an AI Code Mentor.
Part of the DeepLearning.AI offerings on Coursera, this specialization appears to be targeted towards those interested in the intersection of developer relations and the burgeoning field of generative AI.
This course teaches how to build and deploy AI-powered web applications using Python and the Flask framework. It covers the end-to-end lifecycle, including creating APIs, handling requests, and integrating IBM Watson AI libraries.
While not strictly focused on convex/stochastic optimization for ML, this course provides a strong foundation in optimization principles through discrete problems.
A comprehensive course on vision transformers and their use cases in computer vision. You'll explore the rise of transformers and attention mechanisms and gain insights into self-attention, multi-head attention, and the pros and cons of transformers.
This course covers the essential exploratory techniques for summarizing data. It is part of the Data Science Specialization from Johns Hopkins University and focuses on applying these techniques before formal modeling.
Learn to think like a data scientist by using interactive features in MATLAB to explore, analyze, and visualize data. The course focuses on extracting subsets of data, computing statistics, and creating customized visualizations.
This course, offered by Google Cloud, delves into what constitutes a good feature and how to effectively represent it in a machine learning model. It covers essential data processing techniques for preparing a feature set, including preprocessing and feature creation, as well as feature crosses and TensorFlow Transform.
This course, offered by Adobe, focuses on the application of generative AI in design, with a special emphasis on Adobe Firefly. It covers the foundational concepts of generative AI, practical use of Firefly's tools, and the ethical considerations in AI-driven content creation.
A free course that teaches how to use AI for delegating tasks, brainstorming solutions, and freeing up time from repetitive work, utilizing tools like Gemini.
This course explores how generative AI is revolutionizing creative industries. You will learn to use AI to create personalized, interactive experiences and immersive learning environments for games, media, and virtual training.
An intermediate-level course on leveraging Generative AI to improve customer success processes, engagement, and support automation, while addressing ethical and data privacy challenges.
From Johns Hopkins University, this course focuses on the principles of hypothesis testing as applied to public health research questions.
This course introduces the AI and machine learning offerings on Google Cloud for both predictive and generative AI projects. It explores the technologies, products, and tools available throughout the data-to-AI life cycle, covering AI foundations, development, and solutions. The course is aimed at data scientists, AI developers, and ML engineers, offering engaging learning experiences and practical hands-on exercises.
This course covers making inferences from sample data to the broader population. It delves into the principles of significance testing, including p-values, power, and Type I and II errors, and covers a wide range of statistical tests for different data types and research designs.
This course explores the fundamentals of Education Technology, including alternative and digital education, hybrid learning, and the core technologies driving EdTech such as AI, Data, and AR/VR.
This course equips learners with the strategies and tools to design, manage, and scale AI projects in real-world environments. It emphasizes applying agile methodologies and risk mitigation to optimize AI initiatives.
A comprehensive professional certificate that covers the entire UX design process, with a specific focus on accessibility and inclusive design. It also teaches how to leverage AI to enhance UX design efficiency and creativity.
Hello there,Welcome to the “Machine Learning Python with Theoretically for Data Science” course.Machine Learning with Python in detail both practically and theoretically with machine learning project for data scienceMachine learning courses teach you the technology and concepts behind predictive text, virtual assistants, and artificial intelligence. You can develop the foundational skills you need to advance to building neural networks and creating more complex functions through the Python and R programming languages. Machine learning training helps you stay ahead of new trends, technologies, and applications in this field.Machine learning describes systems that make predictions using a model trained on real-world data. For example, let's say we want to build a system that can identify if a cat is in a picture. We first assemble many pictures to train our machine learning model. During this training phase, we feed pictures into the model, along with information around whether they contain a cat. While training, the model learns patterns in the images that are the most closely associated with cats. This model can then use the patterns learned during training to predict whether the new images that it's fed contain a cat. In this particular example, we might use a neural network to learn these patterns, but machine learning can be much simpler than that. Even fitting a line to a set of observed data points, and using that line to make new predictions, counts as a machine learning model.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. machine learning, python, data science, machine learning python, python data science, machine learning a-z, python for data science and machine learning bootcamp, python for data science, complete machine learning, machine learning projects,Use Scikit Learn, NumPy, Pandas, Matpl
This course focuses on uncovering hidden structures from unlabeled data. It covers Principal Component Analysis (PCA) for dimension reduction and popular clustering methods like K-means and hierarchical clustering.
A guided project that teaches how to use PySpark to build a machine learning model for predicting customer churn in a Telecommunications company, covering data loading, exploratory data analysis, preprocessing, model training, and evaluation.
This course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud.
This course introduces the theory behind diffusion models, which are the foundation of many image generation tools, and covers how to train and deploy them on Vertex AI.
An introductory course explaining the importance of responsible AI and how Google implements it in its products. It covers Google's 7 AI principles, providing a high-level understanding of ethical feature use.
This specialization focuses on applying NLP techniques to real-world problems. By the end of the series of courses, you will have hands-on experience in building NLP models for various tasks, including text summarization.
A project-based course that teaches practical techniques for cleaning messy data in Microsoft Excel, including data manipulation and transformation.
An open and free course from Carnegie Mellon University that introduces causal and statistical reasoning for critical thinking.
This course covers the best practices for testing machine learning systems. You'll learn how to design and implement tests for data, models, and infrastructure. The course also covers topics such as fairness, privacy, and security in the context of ML testing.
This course equips you with the skills to analyze, implement, and assess large language models in real-world scenarios. You will learn about core LLM capabilities, summarization, translation, and how LLMs power content generation. The course also covers building chatbots and sentiment analysis tools with LangChain and evaluating LLM performance using benchmarks like ROUGE, GLUE, and BIG-bench.
This certification demonstrates an individual's ability to ensure safety and trust in the development, deployment, and ongoing management of ethical AI systems. It covers foundational knowledge of AI systems, relevant laws and frameworks, the AI life cycle, and risk management.
This certification focuses on integrating AI into product strategy and effectively managing AI projects. It is designed for product managers and leaders aiming to drive AI initiatives, with an emphasis on leadership and strategic decision-making.
Learn MLOps, experiment tracking, model management, and production ML from industry experts.
A 6-week online program for supply chain professionals and business leaders. It covers digital supply chain frameworks, AI applications, and includes case studies and faculty sessions, providing a certificate from MIT xPRO.
Learn about AI applications across industries and the fundamental concepts of Machine Learning and Deep Learning. The course also covers the deployment of AI workloads in various environments, including on-premise, cloud, and hybrid models.
This course provides professionals in the energy sector with an understanding of AI applications. It covers making forecasts about the future performance of energy resources, the use of robotics in harsh environments, and applying machine learning for operational detection.
This specialization covers how data analytics is used to make business decisions and includes components of data exploration and visualization.
This course, part of the 'DeepLearning.AI Data Engineering Professional Certificate', covers designing storage architectures for various use cases. It helps in selecting appropriate technologies and practicing common query patterns. While not exclusively about row, column, and vector stores for ML, it provides the foundational knowledge of data storage principles that are essential for machine learning.
This course focuses on the best practices and tools for deploying, evaluating, monitoring, and operating production machine learning systems on Google Cloud. It provides hands-on practice with Vertex AI Feature Store, including streaming ingestion at the SDK layer. The curriculum is designed to teach learners how to containerize ML workflows for reproducibility and scalability, and how to efficiently manage ML features.
An in-depth exploration of machine learning applications in cybersecurity, focusing on techniques for threat detection and prevention. Participants will gain a solid grounding in machine learning fundamentals, including neural networks, clustering, and support vector machines, tailored specifically for cybersecurity contexts.
Learn LLM Application Development with LangChain
A graduate-level course covering the theory and algorithms for optimization in machine learning. It explores topics like convex and non-convex optimization, gradient-based methods, and stochastic optimization.
Learn about machine learning, AI, and deep learning with Google Cloud. Covers TensorFlow, AutoML, and cloud AI services.
A graduate-level course in machine learning with a focus on fundamental methodologies and algorithms, including Kernel Methods and Support Vector Machines.
Part of the DeepLearning.AI specialization, this course teaches the core concepts of linear algebra and how they are applied in machine learning and data science.
Learn the fundamentals of robotics and how to leverage AWS services for developing and deploying robotics applications.
This course introduces simple and multiple linear regression models, allowing you to assess the relationship between variables in a data set and a continuous response variable.
This course from Google Cloud is designed for business professionals who want to understand how machine learning can be applied to solve business problems. It covers key concepts and use cases of machine learning, including regression.
This course covers the end-to-end process of building and maintaining production ML systems. It includes modules on data needs and modeling strategies, which touch upon the importance of choosing the right data storage and handling evolving data, a key consideration when deciding between row, columnar, and vector-based storage.
The third course in the DeepLearning.AI specialization, focusing on the fundamentals of probability and statistics for machine learning and data science.
This course focuses on the practical application of machine learning techniques in Python. It covers a variety of supervised and unsupervised learning methods and their implementation using the scikit-learn library.
A free, self-paced course developed by Google in collaboration with MIT RAISE. It is designed to help teachers integrate AI into their teaching practices, covering the use of generative AI tools to save time, personalize instruction, and enhance lessons creatively.
A hands-on DataCamp course focused on applying causal inference techniques using Python libraries.
This course covers the basic principles of machine translation, focusing on statistical and neural machine translation, including the current state-of-the-art neural machine translation technology which uses deep learning methods.
This course, part of the Machine Learning Specialization, delves into classification, one of the core areas of machine learning. You'll learn about various classification models, including logistic regression and decision trees, and explore how to handle large-scale classification tasks. The course uses practical case studies like sentiment analysis and loan default prediction to illustrate the concepts.
This course from the University of Colorado Boulder provides a modern take on regression analysis using the R programming language. You will learn about various regression techniques and how to apply them to real-world data.
This course from Johns Hopkins University focuses on the application of multiple regression analysis in the field of public health. You will learn how to analyze and interpret data using regression models.
This course focuses on image processing and computer vision using Keras. It covers techniques for image manipulation, feature extraction, and building image classification models.
This non-technical course equips you with the knowledge to ask the right questions about data and choose the right tools to read, interpret, and communicate data. You'll learn how to get from data to insights and how data drives decision-making.
Basic ideas and techniques underlying the design of intelligent computer systems. Topics include searching, game playing, knowledge representation, logical inference, planning, reasoning under uncertainty, machine learning, and applications.
A certification program for coaches to integrate AI into their practice for personalized, data-driven coaching experiences. The program includes live learning sessions, hands-on AI coaching practice, and peer coaching labs.
This course covers the essential concepts of multivariate calculus required for machine learning, including gradient descent and optimization. It is part of the Mathematics for Machine Learning Specialization.
This course focuses on the analysis of multiple time series simultaneously, covering topics like vector autoregressive (VAR) models.
Learn about the framework of online learning, where algorithms make sequential decisions and learn from feedback.
This course focuses on the practical application of time series analysis. You will learn to analyze sequential data and apply various mathematical models to describe and forecast time series data.
In this course, you will learn to track objects and detect motion in videos. You'll use pre-trained deep neural networks for object detection and optical flow for motion detection. The course project involves tracking cars on a busy highway.
A Duke University course that introduces the fundamentals of probability and data analysis using the R programming language, with a focus on real-world applications.
This course focuses on the data preparation phase of machine learning projects, including techniques for cleaning and transforming data using Python.
A course focused on managing AWS costs effectively. It teaches techniques to reduce spending on AWS resources, covering cost management tools and best practices for cost optimization in the cloud.
Part of the Google Data Analytics Certificate, this course teaches how to check and clean data using spreadsheets and SQL. You will get hands-on practice guided by Google data analysts, learning valuable data cleaning techniques and reporting methods to make data ready for analysis.
Part of the Google Data Analytics Professional Certificate, this course teaches how to clean data using spreadsheets and SQL, covering topics like data integrity, data validation, and handling missing data.
Offered by Vanderbilt University, this course teaches how to write effective prompts for large language models like ChatGPT. You'll learn prompt patterns to unlock powerful capabilities and create complex prompt-based applications.
Part of the IBM Data Science Professional Certificate, this course focuses on data visualization techniques in Python using libraries like Matplotlib, Seaborn, and Folium, which are essential for EDA.
Learn to implement and evaluate Random Forest models for machine learning tasks using Python.
This course covers regression analysis, least squares, and inference using regression models. It also explores special cases of the regression model, such as ANOVA and ANCOVA, and delves into the analysis of residuals and variability.
The fifth course in the Deep Learning Specialization, this course focuses on sequence models for applications like speech recognition, music synthesis, and natural language processing. You will learn to build and train Recurrent Neural Networks (RNNs) and their variants like GRUs and LSTMs.
This highly-rated course on deep learning for computer vision includes a comprehensive module on optimization algorithms for training neural networks.
A skill track on DataCamp that focuses on natural language processing (NLP). It covers techniques for text classification, sentiment analysis, and other NLP tasks, providing a solid foundation in this specialized area of AI.
Learn to train state-of-the-art models in computer vision, NLP, tabular data, and collaborative filtering. No PhD required!
End-to-end machine learning tutorials covering algorithms, projects, deployment, and interview preparation.
While not exclusively an EDA course, it teaches the SQL skills essential for extracting and manipulating data from databases, a crucial first step in any exploratory analysis.
This Stanford University course teaches essential statistical thinking concepts for learning from data. Topics include descriptive statistics, sampling, probability, and regression.
This course focuses on applying statistical methods in R to public health research, covering data management, descriptive statistics, and basic inferential statistics.
Part of the Data Science Specialization from Johns Hopkins University, this course presents the fundamentals of statistical inference in a practical, hands-on manner for data analysis.
Offered by IBM, this course provides a hands-on approach to statistical analysis using Python. It covers descriptive statistics, probability distributions, hypothesis testing, and regression analysis.
A complete curriculum to learn machine learning in 3 months. Includes math, algorithms, and projects.
An intermediate course for developers and designers on architecting immersive VR/AR environments powered by intelligent systems. It covers real-time object detection, voice-enabled NPC interactions, and adaptive UX.
This one-day immersive training is for AI teams to turn vague AI ideas into fundable opportunities. It provides a repeatable recipe to link AI ideas to business goals, validate against user needs, and score what's worth building.
A three-day lecture-based course providing engineers with deep knowledge and hands-on experience with machine learning design and modelling techniques in an industrial context.
This certification program provides knowledge and skills at the intersection of AI and mental health care. It covers innovative AI applications in diagnosing, treating, and monitoring mental health conditions, including chatbots, predictive analytics, and wearable technologies, along with ethical considerations.
This course covers prompt engineering with a section on Multimodal LLMs, including GPT-4V, LLaVA, and Gemini. It explores how these models process different data types and their use cases in image captioning, visual Q&A, and video summarization.
Part of the DeepLearning.AI TensorFlow Developer Professional Certificate, this course teaches you how to solve time series and forecasting problems in TensorFlow. You'll learn best practices for preparing data, and explore how RNNs and ConvNets can be used for prediction.
This course covers time series analysis and mining techniques with a focus on practical applications in R.
A course that covers the fundamentals of vector databases, from embeddings to practical applications. It is part of the DeepLearning.AI offerings on Coursera.
Learn Generative AI for Developers Learning Path
A foundational text on convex optimization by one of the pioneers of the field.
Learn AI-Powered Affiliate Marketing System
A university-level course on information retrieval, with all lecture videos and materials available for free online. The course covers fundamental concepts of search and ranking, including inverted indexes, ranking models like TF-IDF and BM25, and evaluation metrics. The lectures are in-depth and provide a strong theoretical foundation.
This course focuses on optimization techniques with applications in wireless communications, machine learning, and big data.
Welcome to Complete Python Data Science, Deep Learning, R Programming course.Python Data Science A-Z, Data Science with Machine Learning A-Z, Deep Learning A-Z, Pandas, Numpy and R Statistics Data science, python data science, r statistics, machine learning, deep learning, data visualization, NumPy, pandas, data science with r, r, complete data science, maths for data science, data science a-zData Science A-Z, Python Data Science with Machine Learning, Deep Learning, Pandas, Numpy, Data visualization, and RReady for the Data Science career? Are you curious about Data Science and looking to start your self-learning journey into the world of data?Are you an experienced developer looking for a landing in Data Science!In both cases, you are at the right place! The two most popular programming tools for data science work are Python and R at the moment. It is hard to pick one out of those two amazingly flexible data analytics languages. Both are free and open-source.Train up with a top-rated data science course on Udemy. Gain in-demand skills and help organizations forecast product and service demands for the future. From machine learning to data mining to data analysis, we’ve got a data science course to help you progress on your career path.R for statistical analysis and Python as a general-purpose programming language. For anyone interested in machine learning, working with large datasets, or creating complex data visualizations, they are absolutely essential.With my full-stack Data Science course, you will be able to learn R and Python together.If you have some programming experience, Python might be the language for you. R was built as a statistical language, it suits much better to do statistical learning with R programming.But do not worry! In this course, yo
A course that focuses specifically on regression techniques within supervised learning, offered by IBM.
A 3-hour fundamental course that is part of a learning plan to help grow technical skills for building scalable and secure generative AI applications on AWS.
Build 2 complete projects start to finish -- with each step explained thoroughly by instructor Nimish Narang from Mammoth Interactive.Hands-On Neural Networks: Build Machine Learning Models was funded by a #1 project on KickstarterNimish is our cross-platform developer and has created over 20 other courses specializing in machine learning, Java, Android, SpriteKit, iOS and Core Image for Mammoth Interactive. When he's not developing, Nimish likes to play guitar, go to the gym and laze around at the beach. Project #1 -- Learn to construct a model for credit card fraud detection. Our model will take in a list of transactions, some fraudulent and some legitimate. It will output the percentage at which it can calculate fraudulence and legitimacy, how accurate it is. We will also modify the model so that it output whether a specific transaction is fraudulent or legitimate if we pass them in one by one.We will explore a dataset so that you fully understand it, and we will work on it. It's actually pretty hard to find a dataset of fraudulent/legitimate credit card transactions, but we at Mammoth Interactive have found everything for you and curated a step by step curriculum so that you can build alongside us.We will manipulate the dataset so that it will be easy to feed into our model. We will build a computational graph with nodes and functions to run input through the mini neural network.Machine Learning Projects Using Tensorflow -- Mammoth InteractiveProject #2 -- Learn to build a simple stock market prediction model that will predict whether the price stock will go up or down the next morning based on the amount of volume exchange for a given dayAny kind of glo
Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Artificial Intelligence, Machine Learning, Data Science , Auto Ml, Deep Learning, Natural Language Processing (NLP) Web Applications Projects With Python (Flask, Django, Heruko, Streamlit Cloud).How much does a Data Scientist make in the United States?The national average salary for a Data Scientist is US$1,20,718 per year in the United States, 2.8k salaries reported, updated on July 15, 2021 (source: glassdoor)Salaries by Company, Role, Average Base Salary in (USD)Facebook Data Scientist makes USD 1,36,000/yr. Analyzed from 1,014 salaries.Amazon Data Scientist makes USD 1,25,704/yr. Analyzed from 307 salaries.Apple Data Scientist makes USD 1,53,885/yr. Analyzed from 147 salaries.Google Data Scientist makes USD 1,48,316/yr. Analyzed from 252 salaries.Quora, Inc. Data Scientist makes USD 1,22,875/yr. Analyzed from 509 salaries.Oracle Data Scientist makes USD 1,48,396/yr. Analyzed from 458 salaries.IBM Data Scientist makes USD 1,32,662/yr. Analyzed from 388 salaries.Microsoft Data Scientist makes USD 1,33,810/yr. Analyzed from 205 salaries.Walmart Data Scientist makes USD 1,08,937/yr. Analyzed 187 salaries.Cisco Systems Data Scientist makes USD 1,57,228/yr. Analyzed from 184 salaries.Uber Data Scientist makes USD 1,43,661/yr. Analyzed from 151 salaries.Intel Corporation Data Scientist makes USD 1,25,930/yr. Analyzed from 131 salaries.Airbnb Data Scientist makes USD 1,80,569/yr. Analyzed from 122 salaries.Adobe Data Scientist makes USD 1,39,074/yr. Analyzed from 109 salaries.<
This course focuses on topic modeling for marketing data. You will learn to apply topic modeling to various marketing use cases, evaluate and tune topic models, and use them to classify documents. The course covers both traditional and neural network approaches to topic modeling.
This certification provides a comprehensive understanding of machine learning, deep learning, and AI ethics. It is designed for professionals interested in the intersection of AI and blockchain, covering AI integration in business processes, data analytics, and automation with hands-on labs.
Machine learning and Deep learning have revolutionized various industries by enabling the development of intelligent systems capable of making informed decisions and predictions. These technologies have been applied to a wide range of real-world projects, transforming the way businesses operate and improving outcomes across different domains.In this training, an attempt has been made to teach the audience, after the basic familiarity with machine learning and deep learning, their application in some real problems and projects (which are mostly popular and widely used projects).Also, all the coding and implementation of the models are done in Python, which in addition to machine learning, students' skills in Python language will also increase and they will become more proficient in it.In this course, students will be introduced to some machine learning and deep learning algorithms such as Logistic regression, multinomial Naive Bayes, Gaussian Naive Bayes, SGDClassifier, ... and different models. Also, they will use artificial neural networks for modeling to do the projects.The use of effective data sets in different fields, data preparation and pre-processing, visualization of results, use of validation metrics, different prediction methods, image processing, data analysis and statistical analysis are other parts of this course.Machine learning and deep learning have brought about a transformative impact across a multitude of industries, ushering in the creation of intelligent systems with the ability to make well-informed decisions and accurate predictions. These innovative technologies have been harnessed across a diverse array of real-world projects, reshaping the operational landscape of businesses and driving enhanced outcomes across various domains.Within this training course, the primary aim is to impart knowledge to the audience, assuming a foundational understanding of machine learning and deep learning concepts. The focus then
This professional certificate from IBM teaches how to build AI-powered applications using IBM Watson. The program covers natural language processing, computer vision, and building AI-powered chatbots, with a focus on practical application.
A comprehensive program designed to prepare individuals for a career in data analytics. It covers data cleaning, analysis, visualization, and the use of tools like spreadsheets, SQL, R, and Tableau.
Learn Linear Algebra for Machine Learning
This course covers using AI models for image-to-text (vision), text-to-speech, and speech-to-text tasks using the latest APIs. It is part of the 'Getting Started with Generative AI API Specialization'.
This program teaches the fundamentals of data analysis using Excel, Python, SQL, and IBM Cognos Analytics. It includes hands-on projects and a capstone to build a professional portfolio.
This comprehensive program covers the entire data engineering lifecycle. It includes modules on relational databases (row-based), NoSQL data stores (which can be columnar), and big data engines. While not focused on vector databases, it provides a strong foundation in the traditional data storage patterns used for analytics and ML.
A professional certificate program that covers essential machine learning algorithms, including decision trees and ensemble methods.
A 2-hour project-based course where you will learn to understand and write a custom dataset class for an Image-mask dataset. You will also apply segmentation augmentation and load a pretrained state-of-the-art convolutional neural network for segmentation.
This tutorial introduces Neuro-Symbolic AI as a method to enhance Large Language Models (LLMs). It focuses on making LLMs more robust, explainable, and instructable by combining symbolic knowledge structures with statistical learning techniques. The goal is to address the limitations of black-box LLMs, particularly in terms of transparency and domain-specific protocol understanding.
A comprehensive course on leveraging artificial intelligence to enhance marketing strategies, covering content optimization, predictive lead scoring, and campaign analytics.
An article that delves into seven essential AWS services and architectural patterns that solutions architects need to know to successfully design and implement AI-powered solutions in the cloud.
An introductory course to the concepts and terminology of artificial intelligence (AI) and machine learning (ML). Students will be able to select and apply ML services to resolve business problems and will be able to label, build, train, and deploy a custom ML model.
This program provides a deep understanding of how to integrate artificial intelligence into coaching and coaching businesses. It offers practical tools and methods for utilizing AI in daily coaching work.
A practical, step-by-step course designed by grant writing professionals. It focuses on building foundational skills using AI to transform how you work and includes AI tools, prompt guides, and workflow automations. Upon completion, you earn a Professional AI Grant Writer Certificate.
An online course focusing on the application of AI in e-commerce. It includes an in-depth analysis of AI case studies in e-commerce to provide practical insights.
A guide for developers on how to train a machine learning model and deploy it on-device in an iOS app using Core ML. The tutorial covers the entire process from data collection to model execution in Xcode.
An accredited course designed by mental health professionals that provides in-depth and updated insights into the integration of AI in psychology. It covers the contributions of AI to mental health care, ethics, machine learning in psychology, and neuropsychology and AI integration.
This course covers the core concepts of Generative AI and its practical applications in cybersecurity, including hands-on experience with tools for threat detection and mitigation.
This course teaches how to build a competitive intelligence system to uncover market opportunities. You will learn to analyze competitor positioning and strategies, turn insights into actionable growth strategies, and identify market gaps. It is designed for marketers, business strategists, and product teams.
An interactive deep learning book with code, math, and discussions. It includes a chapter on training on multiple GPUs, covering both from-scratch implementations and concise implementations using deep learning frameworks.
This chapter from an online book on Data Science with Python focuses on data wrangling operations using the Pandas library. It covers hierarchical indexing, combining datasets through merging, joining, and concatenating, and reshaping data with pivot and melt functions.
This program explores the principles of a data-driven approach to marketing. Through case studies of companies like Netflix and Booking.com, you will learn to implement innovative marketing analytics initiatives.
This resource discusses the central role of statistics in the data science approach. It emphasizes the need for statistical thinking in designing data collection, deriving insights from data visualization, supporting data-based decisions, and constructing predictive models.
Master strategic digital twin modeling and simulation for industry 4.0 planning and execution. This course will help you build skills for leading successful initiatives in this area.
A training course that highlights the transformative role of AI across underwriting, claims, and product development, with practical applications in fraud detection, predictive modeling, and real-time customer engagement.
A guided project that teaches the fundamentals of using TensorFlow for image classification. You will build, train, and evaluate a neural network to classify images from the CIFAR-10 dataset.
Bienvenidos a este Curso Completo de Data Science en Python Desde Cero. En este curso, aprenderemos cómo usar Python para Data Science. Aprenderemos cómo recopilar datos, limpiar datos, hacer visualizaciones y construir un modelo de machine learning usando Python.El objetivo principal de este curso es llevar tus habilidades analíticas y de programación al siguiente nivel para desarrollar tu carrera como data scientist. Para lograr este objetivo, vamos a resolver +100 ejercicios y varios proyectos que te ayudarán a poner en práctica todos los conceptos de programación utilizados en Data Science.Aprenderemos las principales librerías de Python utilizadas en data science, como Pandas, Numpy y Scikit Learn, y las usaremos para hacer tareas que data scientist realizan a diario (limpieza de datos, visualización de datos, recopilación de datos y creación de modelos).Este curso cubre 4 secciones.1. Curso básico de Python para Data Science: En la primera sección, aprenderemos todos los conceptos básicos de Python que necesita saber para data science. Aprenderemos a usar variables, listas, diccionarios y más.2. Python para análisis de datos: Aprenderemos las librerías de Python que se utilizan para el análisis de datos, como Pandas y Numpy. Ambas son excelentes herramientas para explorar y trabajar con datos. Usaremos Pandas y Numpy para realizar tareas de data science como limpiar y preparar datos.3. Python para visualización de datos: En la tercera sección, aprenderemos como hacer visualizaciones estáticas e interactivas con Pandas. Además, te mostraré algunas técnicas para realizar correctamente la visualización de datos.4. Machine Learning con Python: En la cuarta sección, aprenderemos scikit-learn resolviendo un problema de clasificación de texto en Python. Esta es la librería de machine learning más popular en Python y no solo aprenderemos como implementar algoritmos de machine learning en Python, sino que también aprenderemos conceptos básicos detrás de los a
This course teaches you to create automated systems for 3D point clouds. It covers several segmentation workflows, feature engineering, and applying machine learning classifiers for point-based or object-based classification.
This course explores the transformative role of AI and Machine Learning in modern insurance operations, including underwriting, claims processing, fraud detection, customer engagement, and risk assessment.
La Visión por Computador o Computer Vision (en inglés) es uno de los primeros objetivos que tuvo la programación desde sus inicios y, sobre todo, desde que se planteó la utilización del procesado automático en las cadenas de montaje. Desde discriminar la madurez de las frutas por su color, hasta reconocer patrones biométricos, pasando por los pulsómetros ópticos, o el reconocimiento de matrículas. Las utilidades de la Visión por Computador están sólo limitadas por la imaginación humana. En los últimos años, con el aumento del conocimiento en la denominada Ciencia de los Datos, se han desarrollados nuevos (y no tan nuevos) métodos de Aprendizaje para que sean las máquinas las que puedan tomar decisiones en base al procesado de la imagen que sus ojos tecnológicos les proporciona. El Machine Learning y, el siguiente paso, el Deep Learning ha supuesto una ventaja mayor si cabe en la autonomía de las máquinas. Trabajaremos con un famoso set de datos denominado MNIST, y que contiene 60.000 ejemplos de números manuscritos con su correspondiente etiqueta del número que representan. Cada número esta formado por una matriz de píxeles de 28x28 con valores entre 0 y 255 para la intensidad del trazo. En el curso vamos a analizar una buena cantidad de métodos y algoritmos de Machine Learning, como Naïve Bayes, Random Forest, Support Vector Machine, K Nearest Neighbours o Redes Neuronales y sistemas de pre-procesado de la información, como PCA, SVD o HOG. También trabajaremos algunos sistemas de Deep Learning, como H2O o Tensor Flow (de Google) para el tratamiento de esta información de imágenes. Espero que os guste el curso y que disfrutéis aprendiendo los entresijos de la Visión por Computador y el Aprendizaje Profundo y Automático.
A hands-on AI PM program that covers everything from AI fundamentals to go-to-market strategy. It includes practical labs on building prototypes and testing agentic workflows, with a focus on creating a risk-aware roadmap.
This course, part of the Master in Applied Artificial Intelligence program, covers the fundamentals of theoretical statistics that form the foundation for analyzing machine learning algorithms. Topics include statistical models, inference, maximum likelihood estimation, hypothesis testing, and Bayesian inference.
A tutorial on creating a voice-enabled AI assistant using OpenAI's Realtime API and WebRTC. It covers session management, lifecycle events, and handling real-time audio streaming.
An intensive online certification program on mastering the use of AI in smart energy grids. It covers AI applications for real-time energy monitoring and machine learning algorithms for demand forecasting.
A free virtual course that teaches essential skills in generative artificial intelligence, including fact-checking AI-generated content and ethical AI use. The course is self-paced and designed for learners with no prior experience.
This course explores baseline vision transformer models and their performance on remote sensing image classification. You will gain a good understanding of vision transformers and how to deploy them for remote sensing image classification using PyTorch.
A 4-week online course that provides a comprehensive overview of AI's application in the music and audio industry. Students will learn to create original melodies and beats using AI tools like Google's Magenta Studio and explore AI's impact on genre classification, music recommendation, and audio identification.
A 16-week online certificate program that teaches how to build and apply Generative AI techniques. A project in the course involves developing a Generative AI system to automate the drafting of NIH research proposals.
Science des Données et Apprentissage Automatique : Compréhension Théorique ApprofondieLa science des données (Data Science) est un domaine vaste et fascinant, tandis que l'apprentissage automatique (Machine Learning) est une branche passionnante de la Data Science. Ce cours de deux heures offre une exploration détaillée de ces domaines pour ceux qui souhaitent comprendre leur fonctionnement.Ce cours se distingue par son approche visuelle et simplifiée, qui démystifie les concepts et algorithmes de l'apprentissage automatique sans se perdre dans les détails mathématiques. Il se concentre sur la théorie, offrant une base solide pour quiconque souhaite exceller dans le domaine de la science des données.Les sections de ce cours sont interconnectées et progressives, formant un ensemble cohérent qui facilite l'apprentissage. Chaque section se construit sur les précédentes, vous permettant d'explorer des concepts de plus en plus avancés au fur et à mesure de votre progression.Ce cours aborde les compétences les plus recherchées dans le monde réel de la science des données et de l'apprentissage automatique. Il est conçu pour être simple, facile à comprendre, et descriptif, vous permettant de progresser rapidement.Rejoignez ce cours pour démystifier la science des données et l'apprentissage automatique. C'est une opportunité unique d'acquérir des connaissances solides dans un format accessible et inspirant !Contenu du cours :Après avoir suivi ce cours avec succès, vous serez en mesure de :Comprendre les concepts, principes et théories de la science des données et de l'apprentissage automatiqueAppréhender la méthodologie de la science des données et de l'apprentissage automatiqueÉvaluer les avantages et les inconvénients des différents algorithmes d'apprentissage automatiqueSélectionner l'algorithme d'apprentissage automat
Yapay zeka alanına giriş yapmak ve "öğrenen" uygulamalar geliştirmek istiyorsanız derin öğrenme yöntemlerini öğrenmek için sizi temelden ileri seviyeye kadar teorik anlatım ve pratik uygulamaları içeren bu kapsamlı "Derin Öğrenmeye Giriş" eğitimime davet ediyorum.Eğitimi bitirdiğinizde, derin öğrenmenin temellerini, yapay sinir ağı modelleri oluşturma ve geliştirme adımlarını ve başarılı yapay öğrenme projelerini nasıl gerçekleştirebileceğinizi öğreneceksiniz. Uygulayacağımız yöntemler:Temel yapay sinir ağları, Evrişimli sinir ağları (CNN), Özyinelemeli sinir ağları (RNN), Uzun-kısa vadeli bellek modeli (LSTM), Makine öğrenmesinde optimizasyon ve regülarizasyon yöntemlerini, Kapsül ağları, Pekiştirmeli öğrenme (RL), Çekişmeli üretici ağları (GAN) Tüm bu yöntemleri Python programlama dili kullanarak TensorFlow ve gerisinde çalışan Keras kütüphanelerinde uygulayacaksınız. Yapay zeka ve derin öğrenme çoklu endüstrileri geliştirmekte ve dönüştürmektedir. Bu dersi tamamladıktan sonra, bunu işinize uygulamak için yaratıcı yollar bulabilirsiniz.
This course explores the foundational principles of artificial intelligence and its transformative applications in medicine and biomedical research.
Learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence.
KI Prompt Engineering Techniken für LLMs: Tipps & TricksEntfessele das volle Potenzial von KI-Sprachmodellen!Keine lästigen Notizen während des Kurses!Konzentriere dich ganz auf das Lernen und profitiere von den bereits vorbereiteten Notizen und wissenschaftlichen Quellen im Begleitmaterial.Dieser Kurs ist ideal für:Einsteiger und Entdecker, die KI-Sprachmodelle wie ChatGPT 4, Google Gemini und Bing Chat im Alltag nutzen möchtenBerufstätige, die ihre Ergebnisse mit KI-Tools verbessern möchtenAlle, die mit den Standard-Ergebnissen von KI-Sprachmodellen nicht zufrieden sindLerninhalte:Grundlagen des Prompt Engineering: So steuerst du KI-Sprachmodelle präzise und effektivEinführung in Large Language Models (LLMs): Funktionsweise, Stärken und SchwächenVerschiedene Prompting-Techniken: Rollenzuweisung, Chain-of-Thought, Tree-of-Thought und mehrErweiterte Prompting-Strategien: Kontextualisierung, Kombinieren von Techniken und Nutzung von Prompt GeneratorenPraktische Anwendungen: Fallbeispiele aus verschiedenen BranchenTipps & Tricks: Vermeiden von Fehlern und Optimierung Ihrer PromptsErhalte:Umfassendes Wissen über Prompt Engineering und LLMsPraktische Fähigkeiten zum Erstellen effektiver PromptsZugang zu einer Community von GleichgesinntenExklusives Begleitmaterial:Fertig geschriebene, aufgearbeitete Notizen zu allen theoretischen Kapiteln<p
A nine-module course covering the role of machine learning and AI in financial services. It delves into key methodologies, algorithm selection, and includes case studies with functional code.
An 8-part WhatsApp course that teaches people how to use generative AI to navigate government processes, understand complex documents, and organize for community action.
Para entender cómo organizaciones como Google, Amazon e incluso Udemy utilizan el Machine Learning y la inteligencia artificial (IA) para extraer el significado y los conocimientos de enormes conjuntos de datos , este curso de Machine Learning te proporciona lo esencial. Según Glassdoor y Indeed, los científicos de datos ganaron un sueldo medio de 120.000 dólares, ¡y eso es solo la norma!Cuando se trata de ser atractivo, los científicos de datos ya lo son. En un mercado laboral altamente competitivo, es difícil retenerlos una vez contratados. Las personas con una mezcla única de formación científica, experiencia informática y capacidad de análisis son difíciles de encontrar.Al igual que los "quants" de Wall Street de los años ochenta y noventa, se espera que los científicos de datos de hoy en día tengan un conjunto de habilidades similares. Las personas con formación en física y matemáticas acudieron a los bancos de inversión ya los fondos de cobertura en aquella época porque pudieron idear algoritmos y métodos de datos novedosos.Dicho esto, la ciencia de los datos se está convirtiendo en una de las ocupaciones más adecuadas para el éxito en el siglo XXI. Se trata de una profesión informatizada, basada en la programación y de naturaleza analítica. Por lo tanto, no es de extrañar que la necesidad de científicos de datos haya preocupado en el mercado laboral en los últimos años.La oferta, en cambio, ha sido bastante restringida. Es un reto conseguir los conocimientos y habilidades necesarios para ser contratado como científico de datos .En este curso, las notaciones y matemáticas la jerga se reducen a lo más básico, cada tema se explica en un lenguaje sencillo, lo que facilita su comprensión. Una vez que tengas en tus manos el código, podrás jugar con él y construir sobre él. El énfasis de este curso está en entender y usar estos algoritmos en el mu
Discover the power of building AI-powered applications entirely in Kotlin with our comprehensive course on Jetpack Compose, Langchain4j, and Ollama (Local LLM). Whether you're a seasoned developer or a newcomer, this course equips you with the skills to harness these cutting-edge technologies and stay relevant in this AI driven industry. Throughout the course, we focus on practical hands-on learning, guiding you through the creation of six distinct applications leveraging Langchain4j:Hello World AI: Create an AI that greets users dynamically.Rephaser AI: Transform sentences into various tones for easy clipboard use.Unlost AI: Develop an AI to help users remember where they've placed their belongings.Text Adventure AI: Construct interactive storytelling experiences using AI.ResumeQnA AI: Utilize AI to summarize resumes and retrieve candidate information interactively.RAG Medium Articles AI: Build a Retrieval-Augmented Generation (RAG) AI for summarizing and querying public Medium articles.You'll gain proficiency in Jetpack Compose for designing modern, responsive UIs and harness Langchain4j and Ollama for AI model management and natural language processing tasks. Learn essential Kotlin programming techniques to seamlessly integrate these technologies and optimize performance on local platforms.Join us to unlock the potential of Kotlin-based AI app development with practical skills and real-world applications in demand today.
A free 2-hour course designed to help lawyers and professionals understand how to draft and negotiate AI product contracts. It focuses on identifying risks specific to AI products and advising on operational strategies.
In this course you will learn how to deploy Machine Learning Deep Learning Models using various techniques. This course takes you beyond model development and explains how the model can be consumed by different applications with hands-on examplesCourse Structure:Creating a Classification Model using Scikit-learnSaving the Model and the standard Scaler Exporting the Model to another environment - Local and Google ColabCreating a REST API using Python Flask and using it locallyCreating a Machine Learning REST API on a Cloud virtual serverCreating a Serverless Machine Learning REST API using Cloud FunctionsBuilding and Deploying TensorFlow and Keras models using TensorFlow ServingBuilding and Deploying PyTorch ModelsConverting a PyTorch model to TensorFlow format using ONNXCreating REST API for Pytorch and TensorFlow ModelsDeploying tf-idf and text classifier models for Twitter sentiment analysisDeploying models using TensorFlow.js and JavaScriptTracking Model training experiments and deployment with MLFLowRunning MLFlow on Colab and DatabricksAppendix - Generative AI - Miscellaneous Topics.OpenAI and the history of GPT modelsCreating an OpenAI account and invoking a text-to-speech model from Python codeInvoking OpenAI Chat Completion, Text Generation, Image Generation models from Python codeCreating a Chatbot with OpenAI API and ChatGPT Model using Python on Google ColabChatGPT, Large Language Models (LLM) and prompt engineeringNew Section : Agent-Mode Model Building and Deployment with GitHub CopilotVibe Coding: Model Development with GitHub Copilot Using a Single Prompt<li
Atenção: nesse curso ainda estão sendo adicionadas aulas! Machine Learning (aprendizado de máquina) é uma área que representa uma evolução nos campos de Ciência da Computação, Análise de Dados, Engenharia de Software e Inteligência Artificial. Nesse curso você aprenderá Machine Learning com a linguagem de Programação Python. Não é preciso ter conhecimento em Python, pois o curso possui uma seção para quem é iniciante na linguagem. Além disso, o curso trata das principais bibliotecas para análise de dados e utilização de técnicas de aprendizado de máquina tais como NumPy, Pandas, scikit-learn e Matplotlib. Também serão explicadas técnicas de aprendizado de máquina para facilitar o entendimento e utilização das mesmas nos exemplos práticos. Todo o curso é 100% em vídeo-aulas, tem direito a certificado e acesso vitalício! Os instrutores Marcos Castro (mais de 12 mil alunos na Udemy) e Gileno Filho (mais de 10 mil alunos na Udemy) irão estar disponíveis para tirar quaisquer dúvidas através do fórum do curso. O que está esperando? Machine Learning é utilizado por empresas ao redor do mundo para facilitar a análise de dados. Vivemos a era do Big Data, o volume de dados produzidos é gigantesco e precisamos de técnicas para automatizar e nos ajudar a encontrar algum padrão nesses dados de forma que possamos resolver os problemas. Aguardamos você no curso!
Let me tell you my story. I graduated with my Ph. D. in computational nano-electronics but I have been working as a data scientist in most of my career. My undergrad and graduate major was in electrical engineering (EE) and minor in Physics. After first year of my job in Intel as a "yield analysis engineer" (now they changed the title to Data Scientist), I literally broke into data science by taking plenty of online classes. I took numerous interviews, completed tons of projects and finally I broke into data science. I consider this as one of very important achievement in my life. Without having a degree in computer science (CS) or a statistics I got my second job as a Data Scientist. Since then I have been working as a Data Scientist. If I can break into data science without a CS or Stat degree I think you can do it too! In this class allow me sharing my journey towards data science and let me help you breaking into data science. Of course it is not fair to say that after taking one course you will be a data scientist. However we need to start some where. A good start and a good companion can take us further.We will definitely discuss Python, Pandas, NumPy, Sk-learn and all other most popular libraries out there. In this course we will also try to de-mystify important complex concepts of machine learning. Most of the lectures will be accompanied by code and practical examples. I will also use “white board” to explain the concepts which cannot be explained otherwise. A good data scientist should use white board for ideation, problem solving. I also want to mention that this course is not designed towards explaining all the math needed to “practice” machine learning. Also, I will be continuously upgrading the contents of this course to make sure that all the latest tools and libraries are taught here. Stay tuned!
This course provides participants with the knowledge and tools to harness technology responsibly, emphasizing how AI and ICT innovations improve decision-making, streamline operations, and foster efficiency in governance.
Este curso básico de TensorFlow te enseñará a crear redes neuronales para Deep Learning o aprendizaje profundo.Es una guía fácil con muchos ejemplos, para entener las complejidades del marco de TensorFlow de Google.Este curso está repleto de ejemplos escritos en Python sobre Jupyter Notebook, para que puedas probarlos tu mismo.Estos son los temas tratados en este curso de TensorFlow :- Introduccion al Machine Learning- Instalacion del entorno de trabajo- Curso básico de Python sobre las librerías usadas en este curso: - NumPy - Pandas - Matplotlib - SciKit Learn- Introducción a las redes neuronales (Deep Learning) - Neuronas y perceptrones - Funciones de activacion - Funciones de coste - Algoritmo del gradiente descendiente - Practicar con una red neuronal en el navegador- TensorFlow - Introducción a TensorFlow - Sintaxis básica de TensorFlow - Grafos en TensorFlow - Grafos por defecto - Variables y placeholders - Ejemplo de red neuronal - parte 1 - Ejemplo de red neuronal - parte 2 - Ejemplo de regresión simple con TensorFlow - Ejemplo de clasificación con TensorFlow - Ejemplo de regresión con TensorFlow - parte 1 - Ejemplo de regresión con TensorFlow - parte 2 - Ejemplo de regresión con TensorFlow - parte 3- Redes Neuronales Convolucionales - Introducción a las redes neuronales convolucionales - MNIST - Base de datos de imágenes de dígitos escritos a mano - Ejemplo con MNIST - Importar base de datos y mostrar una imagen- Redes Neuronales Recurrentes - Introducción a las redes neuronales recurrentes - Ejemplo de una red neuronal recurrente con TensorFlow - Ejemplo de series temporales - parte 1 - Ejemplo de series temporales - parte 2 - Ejemplo de series temporales - parte 3- Bibiliotecas - Estimator API - Ke
A foundational book on kernel methods, providing a comprehensive overview of the theory and algorithms. While not a course, it is a key resource for in-depth learning.
This program includes modules on data exploration and statistical inference, where students perform statistical analysis on real-world datasets and build and validate hypotheses using statistical tests.
The first course in the HarvardX Data Science Professional Certificate, it provides the foundational R programming skills necessary for data wrangling and exploration.
A course that provides a solid foundation in optimization theory and algorithms.
edX offers various courses on time series analysis from different universities. This is a general link to search for the latest offerings.
A hands-on lab from Google Cloud that walks you through the process of creating a video dataset, training an AutoML video classification model, and deploying it for batch predictions using Vertex AI.
This course explores various ensemble techniques, including bagging, boosting, and stacking, to improve the performance of your machine learning models.
A hands-on course focused on building LLM applications using Vertex AI and Gemini models, covering prompt templating and integration into serverless architectures.
Hi there,Welcome to my " Data Science | The Power of ChatGPT in Python & Data Science " course.Data Science & ChatGPT | Complete Hands-on Python Training using Chat GPT with Data Science, AI, Machine LearningData science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.Python instructors at OAK Academy specialize in everything from software development to data analysis and are known for their effective, friendly instruction for students of all levels.Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python, python programming, python examples, python example, python hands-on, pycharm python, python pycharm, python with examples, python: learn python with real python hands-on examples, learn python, real pythonPython's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.ChatGPT is a prototype AI chatbot developed by OpenAI that specializes in conversation. A chatbot is a large language model that has been fine-tuned with both supervis
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.In this course you will build MULTIPLE practical systems using natural language processing, or NLP - the branch of machine learning and data science that deals with text and speech. This course is not part of my deep learning series, so it doesn't contain any hard math - just straight up coding in Python. All the materials for this course are FREE.After a brief discussion about what NLP is and what it can do, we will begin building very useful stuff. The first thing we'll build is a cipher decryption algorithm. These have applications in warfare and espionage. We will learn how to build and apply several useful NLP tools in this section, namely, character-level language models (using the Markov principle), and genetic algorithms.The second project, where we begin to use more traditional "machine learning", is to build a spam detector. You likely get very little spam these days, compared to say, the early 2000s, because of systems like these.Next we'll build a model for sentiment analysis in Python. This is something that allows us to assign a score to a block of text that tells us how positive or negative it is. People have used sentiment analysis on Twitter to predict the stock market.We'll go over some practical tools and techniques like the NLTK (natural language toolkit) library and latent semantic analysis or LSA.Finally, we end the course by building an article spinner. This is a very hard problem and even the most popular products out there these days don't get it right. These lectures are designed to just get you started and to giv
Data Scientist wurde von Glassdoor als Nummer 1 Job gerankt und erzielt laut Indeed einen überdurchschnittlichen Gehalt. Die Karriere im Bereich Data Science ist eine bereichernde Tätigkeit und erlaubt es euch an den größten und interessantesten Herausforderungen der Welt zu arbeiten. Dieser Kurs richtet sich sowohl an Anfänger, die zum ersten Mal mit der Programmiersprache R in Berührung kommen, als auch für erfahrene Entwickler, die ihr Portfolio um Fähigkeiten in Richtung R, Data Sciene und Machine Learning ausbauen wollen! "Perfekter Einstieg in die Sprache R. Zuvor hatte ich keine Kenntnis dieser Sprache. Gut gefällt mir, dass direkt auch Data Science Anwendungen inbegriffen sind, da ich diese beruflich brauche. Top! (★★★★★ D. Mika)Dieser umfangreiche Kurs ist vergleichbar mit anderen Data Science Bootcamps die mehrere tausend Euro kosten. Das alles findest du in über 120 HD Video Lektionen und detaillierten Code Notebooks zu jeder Lektion. Dies macht diesen Kurs zum umfangreichsten Data Science Kurs mit R auf Udemy!Wir werden gemeinsam lernen, wie man mit R programmiert, grandiose Visualisierungen erstellt und mit echten Daten und echte Data Science Fälle umgeht. Dazu verwenden wir R-Studio und das Jupyter Notebook mit R. Hier ist eine Übersicht einiger Themen:Programmieren mit RFortgeschrittene Programmierung in RR Date Frames zur Lösung komplexer Aufgaben verwendenMit R Excel Datein bearbeitenWeb Scraping mit RR mit SQL verbindenGGPlot2 zur Visualisierung verwendenÜbersicht und Einsatz von DplyR und TidyRPlotly für interaktive Visualisierungen verwendenAnalysiere echte Daten an&
Welcome to the course on Data Science & Deep Learning for Business™ 20 Case Studies!This course teaches you how Data Science & Deep Learning can be used to solve real-world business problems and how you can apply these techniques to 20 real-world case studies. Traditional Businesses are hiring Data Scientists in droves, and knowledge of how to apply these techniques in solving their problems will prove to be one of the most valuable skills in the next decade!What student reviews of this course are saying, "I'm only half way through this course, but i have to say WOW. It's so far, a lot better than my Business Analytics MSc I took at UCL. The content is explained better, it's broken down so simply. Some of the Statistical Theory and ML theory lessons are perhaps the best on the internet! 6 stars out of 5!""It is pretty different in format, from others. The appraoch taken here is an end-to-end hands-on project execution, while introducing the concepts. A learner with some prior knowledge will definitely feel at home and get to witness the thought process that happens, while executing a real-time project. The case studies cover most of the domains, that are frequently asked by companies. So it's pretty good and unique, from what i have seen so far. Overall Great learning and great content."--"Data Scientist has become the top job in the US for the last 4 years running!" according to Harvard Business Review & Glassdoor.However, Data Science has a difficult learning curve - How does one even get started in this industry awash with mystique, confusion, impossible-looking mathematics, and code? Even if you get your feet wet, applying your newfound Data Science knowledge to a real-world problem is even more confusing.This course seeks to fill all those gaps in knowledge that scare off
Neste curso, exploramos o vasto mundo de Data Science e Machine Learning, focando na base lógica e matemática por trás dos principais algoritmos utilizados na área. Veremos como funcionam os principais algoritmos de Regressão, Classificação, Clusterização, NLP, Deep Learning, Regras de Associação, Algoritmos Genéticos, Séries Temporais e muito mais - sem exagerar no "matematiquês". O curso foi pensado de forma a ser o mais democrático possível, servindo como porta de entrada para pessoas que queiram aprender de verdade os principais conceitos antes de entrar no mercado, pessoas que já estejam trabalhando com ciência de dados mas se veem com dificuldades de entender como funcionam os modelos, ou pessoas que simplesmente se interessam pela área e gostariam de aprender como funciona - não necessariamente visando adentrar o mercado. Até por isso, o curso não é tão orientado a código; ao invés de criar código para cada modelo e cada técnica mostrada, ao final do curso há uma seção com alguns projetos da vida real, em que podemos ver tanto como o código é feito, mas, principalmente, como é o raciocínio e as decisões tomadas para resolver problemas de dados.Também trago uma seção bastante rica e dedicada a explicar como se "produtizam" modelos em empresas, falando sobre coisas como deploy, monitoramento, construção de features, pré-processamento, definição de um projeto de ML, expectativa e visão do mercado, progressão de carreira e muito mais!O curso ainda tem um "crash course" de Python, opcional para quem já programa na linguagem, mas valiosa para aqueles que precisam de uma base mais sólida.
This course covers the fundamentals of convex optimization and approximation methods.
A comprehensive program designed to prepare you for the field of artificial intelligence and machine learning. It covers designing scalable AI & ML infrastructure, core algorithms, AI agent development, and leveraging cloud-based AI & ML services, specifically through Microsoft Azure. A capstone project simulates real-world challenges.
In this course I will cover, how to develop a Sentiment Analysis model to categorize a tweet as Positive or Negative using NLP techniques and Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.This course will walk you through the initial data exploration and understanding, data analysis, data pre-processing, data preparation, model building, evaluation and deployment techniques. We will explore NLP concepts and then use multiple ML algorithms to create our model and finally focus into one which performs the best on the given dataset.At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1 : Installing Packages.Task 2 : Importing Libraries.Task 3 : Loading the data from source.Task 4 : Understanding the dataTask 5 : Preparing the data for pre-processingTask 6 : Pre-processing steps overviewTask 7 : Custom Pre-processing functionsTask 8 : About POS tagging and Lemmatization Task 9 : POS tagging and lemmatization in action.Task 10 : Creating a word cloud of positive and negative tweets.Task 11 : Identifying the most frequent set of words in the dataset for positive and negative cases.Task 12 : Train Test SplitTask 13 : About TF-IDF VectorizerTask 14 : TF-IDF Vectorizer in actionTask 15 : About Confusion MatrixTask 16 : About Classification ReportTask 17 : About AUC-ROCT
Course Workflow:This course is focused on Embedded Deep learning in Python . Raspberry PI 4 is utilized as a main hardware and we will be building practical projects with custom data .We will start with trigonometric functions approximation . In which we will generate random data and produce a model for Sin function approximationNext is a calculator that takes images as input and builds up an equation and produces a result .This Computer vision based project is going to be using convolution network architecture for Categorical classificationAnother amazing project is focused on convolution network but the data is custom voice recordings . We will involve a little bit of electronics to show the output by controlling our multiple LEDs using own voice .Unique learning point in this course is Post Quantization applied on Tensor flow models trained on Google Colab . Reducing size of models to 3 times and increasing inferencing speed up to 0.03 sec per input . Sections :Non-Linear Function ApproximationVisual CalculatorCustom Voice Controlled LedOutcomes After this Course : You can create Deep Learning Projects on Embedded HardwareConvert your models into Tensorflow Lite modelsSpeed up Inferencing on embedded devicesPost QuantizationCustom Data for Ai ProjectsHardware Optimized Neural NetworksComputer Vision projects with OPENCVDeep Neural Networks with fast inferencing SpeedHardware RequirementsRaspberry PI 412V Power Bank2 LEDs ( Red and Green )Jumper Wires Bread
A hands-on project where you'll train a Bidirectional Neural Network and LSTM based deep learning model to detect fake news from a given news corpus. This is a practical skill for media companies to automatically predict the authenticity of news articles.
A guided project on Coursera where you learn to perform text summarization using the Langchain framework and Generative AI models. The project involves building a web application with Streamlit to make the summarization functionality interactive.
A project that teaches you how to build popular ensemble methods like Bagging and AdaBoost from scratch in Python.
Data science is the field that encompasses the various techniques and methods used to extract insights and knowledge from data. Machine learning (ML) and deep learning (DL) are both subsets of data science, and they are often used together to analyze and understand data.In data science, ML algorithms are often used to build predictive models that can make predictions based on historical data. These models can be used for tasks such as classification, regression, and clustering. ML algorithms include linear regression, decision trees, and k-means.DL, on the other hand, is a subset of ML that is based on artificial neural networks with multiple layers, which allows the system to learn and improve through experience. DL is particularly well-suited for tasks such as image recognition, speech recognition, and natural language processing. DL algorithms include convolutional neural networks (CNNs) and recurrent neural networks (RNNs).In a data science project, DL models are often used in combination with other techniques such as feature engineering, data cleaning, and visualization, to extract insights and knowledge from data. For instance, DL models can be used to automatically extract features from images, and then these features can be used in a traditional ML model.In summary, Data science is the field that encompasses various techniques and methods to extract insights and knowledge from data, ML and DL are subsets of data science that are used to analyze and understand data, ML is used to build predictive models and DL is used to model complex patterns and relationships in data. Both ML and DL are often used together in data science projects to extract insights and knowledge from data.IN THIS COURSE YOU WILL LEARN ABOUT :Life Cycle of a Data Science Project.Python libraries like Pandas and Numpy used extensively in Data Science.Matplotlib and Seaborn for Data Visualizatio
Join our interactive course, "Complete Guide to ChatGPT & Copilot for Python & R Projects", designed to give you hands-on experience in solving real data science problems using AI, Copilot, and ChatGPT. With Udemy's 30-day money-back guarantee in place, there's no need to worry if the class doesn't meet your expectations.The course is taught both in Python and R with RStudio. A complete installation guide on how to install and configure Python and R, was added in April 2024. It also explains how to connect RStudio to Python and use it as Python's IDE.Each lesson in this course stands alone, focusing on a different data science challenge.You'll learn how to apply AI tools like Copilot and ChatGPT to navigate through these challenges efficiently.Incorporating AI tools like Copilot and ChatGPT into your data science workflow can significantly enhance your speed and efficiency, often doubling (X2) or even increasing productivity tenfold (X10), depending on the task at hand.Here's what we'll cover, using practical, project-based learning:Data Clean-up and Tidy: Learn how to organize your data neatly, making it ready for analysis. This is a key step in data science to ensure your data is accurate and easy to work with. Using Pandas with Python and dplyr with R.Load Files of Different Formats: Discover how to bring in data from different kinds of files. This skill is important in data science because it lets you work with all sorts of data in tools like Copilot and ChatGPT.Data Visualization with Graphs: Find out how to use graphs to show your data in a clear and interesting way. Graphs help you see patterns and important points, which is a big part of data science.</
Learn Artificial Intelligence A-Z: Learn How To Build An AI
The ChatGPT Prompt Engineering Playbook - How To Maximize The Power Of Generative AI PromptsATTENTION SOLOPRENEURS: Are You Ready to take your business to the next level by quickly and easily creating all your marketing materials from your Web Page, all the way through to your autoresponder emails, with Artificial Intelligence? You want to do this so you can keep one step ahead of your competitors! And without spending a fortune on software! Is this what you would like to achieve?Then "Give Me A Couple Of Hours Of Your Time And I’ll Show You How to Correctly Use AI Tools, specifically the ChatGPT chatbot, even though these principles Apply In All AI tools, In Your Online Business"In this Course, You’ll Find Out How To Use AI Prompts To Get More Out of Content Creation ActivitiesOur focus is to teach you how to correctly use the concept of conversation with a machine, your computer, to have the machine return back to you exactly what you need.There is a tremendous demand today, and that demand is growing exponentially, for people who understand the “right way” to ask / prompt, A.I. tools to return correct, accurate, and verified information back to them.Let me ask you this question. Does the following sound like you?· You have been “dreaming” about a way that you can enter a few lines of text into your computer, and then with a click of the “enter” key have your computer generate all of your marketing materials?· You hate staring at a blank page and wondering, “Where do I start.”· You want content for your marketing web page that will sell your product when someone enters into it, but what you always end up with never seems to achieve the results you desire. But do not know how to correctly ask for that information.· You want content for your Lead Magnet Thankyou emails that will cause a desire in your customer to come back to your page for more.· You want content for your relationshi
Recent UpdatesJuly 2024: Added a video lecture on hybrid approach (combining clustering and non clustering algorithms to identify anomalies)Feb 2023: Added a video lecture on "Explainable AI". This is an emerging and a fascinating area to understand the drivers of outcomes. Jan 2023: Added anomaly detection algorithms (Auto Encoders, Boltzmann Machines, Adversarial Networks) using deep learningNov 2022: We all want to know what goes on inside a library. We have explained isolation forest algorithm by taking few data points and identifying anomaly point through manual calculation. A unique approach to explain an algorithm!July 2022: AutoML is the new evolution in IT and ML industry. AutoML is about deploying ML without writing any code. Anomaly Detection Using PowerBI has been added. June 2022: A new video lecture on balancing the imbalanced dataset has been added.May 2022: A new video lecture on PyOD: A comparison of 10 algorithms has been addedCourse DescriptionAn anomaly is a data point that doesn’t fit or gel with other data points. Detecting this anomaly point or a set of anomaly points in a process area can be highly beneficial as it can point to potential issues affecting the organization. In fact, anomaly detection has been the most widely adopted area with in the artificial intelligence - machine learning space in the world of business. As a practitioner of AI, I always ask my clients to start off with anomaly detection in their AI journey because anomaly detection can be applied even when data availability is limited.Anomaly detection can be applied in the following areas:Predictive maintenance in the manufacturing ind
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, Gemini Pro, Llama 3, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Hello friends!Welcome to Data Science: Transformers for Natural Language Processing.Ever since Transformers arrived on the scene, deep learning hasn't been the same.Machine learning is able to generate text essentially indistinguishable from that created by humansWe've reached new state-of-the-art performance in many NLP tasks, such as machine translation, question-answering, entailment, named entity recognition, and moreWe've created multi-modal (text and image) models that can generate amazing art using only a text promptWe've solved a longstanding problem in molecular biology known as "protein structure prediction"In this course, you will learn very practical skills for applying transformers, and if you want, detailed theory behind how transformers and attention work.This is different from most other resources, which only cover the former.The course is split into 3 major parts:Using TransformersFine-Tuning TransformersTransformers In-DepthPART 1: Using TransformersIn this section, you will learn how to use transformers which were trained for you. This costs millions of dollars to do, so it's not something you want to try by yourself!We'll see how these prebuilt models can already be used for a wide array of tasks, including:text classification (e.g. spam detection, sentiment analysis, document categorization)named entity recognitiontext summarizationmachine transla
„Künstliche Intelligenz in der Praxis: ChatGPT u. Prompt Engineering“In diesem Kurs erhalten die Teilnehmer eine fundierte Einführung in die Theorie und Praxis der Künstlichen Intelligenz (KI) mit besonderem Fokus auf das Sprachmodell ChatGPT von OpenAI. Dieser Kurs vermittelt detailliertes Wissen über die grundlegenden Konzepte des maschinellen Lernens und der Sprachverarbeitung und beleuchtet, wie KI-Modelle wie ChatGPT zur Optimierung von Geschäftsprozessen genutzt werden können. Dabei werden sowohl die technischen Mechanismen hinter ChatGPT – wie die innovative Transformer-Architektur – als auch die praktischen Anwendungen und Optimierungspotenziale von KI im betrieblichen Alltag anschaulich erläutert.Kursinhalte:Einführung in die Künstliche Intelligenz und maschinelles LernenGrundlagen der Sprachverarbeitung und Funktionsweise der Transformer-ArchitekturPraktische Anwendungsbereiche von ChatGPT: Kundenservice, interne Kommunikation und WissensmanagementDatenanalyse und Mustererkennung zur Identifikation von OptimierungspotenzialenImplementierung von ChatGPT zur Effizienzsteigerung: Automatisierung von Routineaufgaben und Verbesserung der ReaktionszeitenEthische und rechtliche Aspekte der KI-Nutzung, insbesondere Datenschutz und DSGVO-KonformitätLernziele: Am Ende dieses Kurses verstehen die Teilnehmer die Funktionsweise und Potenziale von ChatGPT und ähnlichen KI-Systemen und können deren Einsatz für die Prozessoptimierung in verschiedenen Bereichen bewerten. Sie erlernen, wie sie KI-Technologien in der Praxis sicher und effektiv einsetzen, Prozesse analysieren und potenzielle Effizienzsteigerungen identifizieren können. Zusätzlich erhalten sie das nötige Bewusstsein für die ethischen und rechtlichen Anforderungen, die den Einsatz von KI-Systemen begleiten.Zielgruppe: Dieser Kurs richtet sich an Fach- und Fü
Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs?Do you want to learn about state of the art Machine Learning frameworks while segmenting cancer in CT-images?Then this is the right course for you!Welcome to one of the most comprehensive courses on Deep Learning in medical imaging!This course focuses on the application of state of the art Deep Learning architectures to various medical imaging challenges.You will tackle several different tasks, including cancer segmentation, pneumonia classification, cardiac detection, Interpretability and many more.The following topics are covered:NumPyMachine Learning TheoryTest/Train/Validation Data SplitsModel Evaluation - Regression and Classification TasksTensors with PyTorchConvolutional Neural NetworksMedical ImagingInterpretability of a network's decision - Why does the network do what it does?A state of the art high level pytorch library: pytorch-lightningTumor SegmentationThree-dimensional dataand many moreWhy choose this specific Deep Learning with PyTorch for Medical Image Analysis course ?This course provides unique knowledge on the application of deep learning to highly complex and non-standard (medical) problems (in 2D and 3D) All lessons include clearly summarized theory and code-along examples, so that you can understand and follow every step. Powerful online community with our QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on our Discord Server.You will learn skills and techniques that the vast majority of AI engineers do not have!</ul
Uniform modeling (i.e. models from a diverse set of libraries, including scikit-learn, statsmodels, and tensorflow), reporting, and data visualizations are offered through the Scalecast interfaces. Data storage and processing then becomes easy as all applicable data, predictions, and many derived metrics are contained in a few objects with much customization available through different modules.The ability to make predictions based upon historical observations creates a competitive advantage. For example, if an organization has the capacity to better forecast the sales quantities of a product, it will be in a more favorable position to optimize inventory levels. This can result in an increased liquidity of the organizations cash reserves, decrease of working capital and improved customer satisfaction by decreasing the backlog of orders. In the domain of machine learning, there’s a specific collection of methods and techniques particularly well suited for predicting the value of a dependent variable according to time, ARIMA is one of the important technique.LSTM is the Recurrent Neural Network (RNN) used in deep learning for its optimized architecture to easily capture the pattern in sequential data. The benefit of this type of network is that it can learn and remember over long sequences and does not rely on pre-specified window lagged observation as input. The scalecast library hosts a TensorFlow LSTM that can easily be employed for time series forecasting tasks. The package was designed to take a lot of the headache out of implementing time series forecasts. It employs TensorFlow under-the-hood. Some of the features are:Lag, trend, and seasonality selectionHyperparameter tuning using grid search and time seriesTransformationsScikit models ARIMALSTMMultivariate- Assignment
Learn the fundamentals of time series analysis using the R programming language, covering data manipulation, visualization, and modeling.
A 2-hour project-based course where you will learn to perform anomaly detection using PyCaret, a low-code machine learning library in Python.
Note: Have opened many videos for preview please only enroll if you follow the preview video's , Any suggestions or modifications ping me in Q&A will respond within 3 business days in most times worst case 7 days if i am travelling Current Topics What are Transformers? (Technical) Concept of RNN3 main concepts in Transformers Positional Encoding Attention Self-Attention What is ChatGPT? (Technical) How You Can Use ChatGPT (Non-Technical) Creating your Generative Pre-trained Transformer 3 (GPT-3) account Basic Querying Generative Pre-trained Transformer 3 (GPT-3) and how ethical principles are upheld Prompt Engineering (Technical) Best Practices for Prompt EngineeringOpen AI Models (Technical) We will explore when to use OPENAI models GPT3 CodexContent FilterParameters in Playground (Technical) Temperature Max Tokens Stop SequenceTop-PInjecting Start & Restart TextFrequency and Presence PenaltyBest ofShow ProbabilitiesHow is ChatGPT Trained? (Technical) What is Generative Pre-trained Transformer 3 (GPT-3) and how is it different from rest of transformers ChatGPT- AI Take Over ? (Non-Technical) Money making ideas with ChatGPT (Non-Technical) fiverr exampleWRITING & TRANSLATIONSocial Media Marketing Art
Machine Learning é uma disciplina da área da Inteligência Artificial que, por meio de algoritmos, dá aos computadores a capacidade de identificar padrões em dados massivos e fazer previsões (análise preditiva).Data Science é o estudo disciplinado dos dados e informações inerentes ao negócio e todas as visões que podem cercar um determinado assunto. É uma ciência que estuda as informações, seu processo de captura, transformação, geração e, posteriormente, análise de dados para converter em evidência.A Análise de Dados é um processo de inspeção, limpeza, transformação e modelagem de dados com o objetivo de descobrir informações úteis, informar conclusões e apoiar a tomada de decisões. A análise de dados tem múltiplas facetas e abordagens, abrangendo diversas técnicas sob uma variedade de nomes, e é usada em diferentes domínios dos negócios, ciências e ciências sociais. No mundo dos negócios de hoje, a análise de dados desempenha um papel tornando a tomada de decisões mais científicas e ajudando as empresas a operar com mais eficáciaNeste curso você vai entender que juntos a Data Science, Machine Learning e Data Analytics além de inovações tecnológicas são aliados para o bom funcionamento das ações organizacionais, e tem poder de influência em toda cadeia produtiva.Bons estudos!
Dans ce cours, tu plongeras dans le monde de l'IA générative avec les LLMs (Large Language Models), en explorant le potentiel de la combinaison de LangChain avec Python. Ce cours complet est conçu pour t'apprendre à exploiter RAPIDEMENT la puissance de la bibliothèque LangChain pour des applications LLMs. Il te permettra d'acquérir les compétences et les connaissances nécessaires pour développer des solutions LLM de pointe pour une gamme variée de sujets.Langchain est un framework pour faire évoluer les LLMs comme ChatGPT avec des sources de données externes et des APIs pour améliorer les capacités. Les composants clés de LangChain, tels que les chaînes, les modèles, les outils et les agents seront présentés, ainsi que la manière de les utiliser pour développer des solutions NLP robustes. Le concept de RAG (Retrieval-Augmented Generation) sera exploré, y compris les processus de stockage et de récupération de l'information. Tu apprendras à mettre en œuvre des magasins vectoriels et à comprendre l'importance des embeddings et la manière de les utiliser efficacement. Nous montrerons également comment utiliser RAG pour interagir avec des documents PDF et des pages web. En outre, tu auras l'occasion d'explorer l'intégration d'agents et d'outils, comme l'utilisation de LLM pour effectuer des recherches sur le web et récupérer des informations récentes.En guise de projet, tu apprendras à créer un chatbot personnalisé qui cherche sur le web et dotée d'une mémoire pour les questions-réponses. De plus, tu appliqueras le RAG pour lire et questionner n'importe quel type de PDFs.Tu apprendras :Ce qu'est LangChain et comment il élargit ce que les assistants d'IA peuvent exploiterIntégrer LangChain à OpenAi, MistralAI et Hugging Face de m
A hands-on project-based course where you build a Support Vector Machine classifier using scikit-learn and the RBF Kernel to predict heart disease. It focuses on the practical implementation and evaluation of SVMs.
A hands-on project-based course that teaches you how to use Facebook's Prophet library for time series forecasting.
This specialization from the University of Colorado Boulder covers various aspects of business analytics, with a strong emphasis on statistical modeling and data-driven decision making.
Modules 2 et 3 sous l’ancienne version de ChatGPT. Cependant, seule l’interface change. 95% du contenu est toujours d’actualité et le module 1 présente les mécanismes de l’IA de manière intemporelle.ChatGPT et l'IA générative, c'est comme Excel ou la boîte email, ne pas apprendre à l'utiliser, c'est ne pas comprendre l'un des plus gros enjeu stratégique de notre époque. Ici, pas de "Guide ULTIME" ou de titre accrocheur mensonger ! Découvrez la révolution ChatGPT à travers cette formation 3 modules complets en 1 qui se différencie par son approche complète incluant dans le premier module une explication complète des fondations de ce système, connaissance clé qui vous permettra de disposer d'un avantage certain sur les autres, suivi de deux modules pratiques et précis sur l'ensemble des fonctionnalités de ChatGPT et des meilleures techniques OFFICIELLES de Prompt Engineering.Avec nous, vous évoluerez du niveau de débutant ou intermédiaire standard au niveau intermédiaire avancé grâce à nos 3 modules complets répartis en 11 sections et déclinés en 74 sessions vidéos accompagnées de quiz et d’exercices pratiques. Vérifiez les notes et les commentaires attribués à ce cours, vous serez ainsi rassurés sur la pertinence et la qualité de cette formation.Lisez attentivement ce que nous vous proposons et n'hésitez pas à comparer notre formation avec la concurrence, nous vous garantissons que l’écrasante majorité des autres formateurs ne proposerons pas de cours aussi complet.Ce qui différencie ce cours des autres, c'est son approche étape par étape et en profondeur dans les méandres de l'IA générative qui vous éclaircira bien des concepts et vous dotera de solides connaissances.Ce Que Vous Obtiendrez
This specialization from the University of Pennsylvania covers the fundamentals of using Big Data, Artificial Intelligence, and Machine Learning to support business. It includes modules on effective marketing strategies using data analytics and how personalization can enhance the customer journey.
This course teaches how to implement machine learning use cases for marketing in Python, including predicting customer churn, measuring and forecasting customer lifetime value, and building customer segments.
Designed for fashion and retail professionals, this course explores the transformative potential of emerging technologies including AI. It focuses on leveraging these technologies to tackle industry challenges, boost revenues, and stay ahead in an evolving digital landscape, including using AI in collection design.
Este curso pretende ser una introducción a las técnicas más relevantes de Machine Learning y mostrar ejemplos de aplicación de estas técnicas. Que sirva para conocer qué técnicas existen, en qué se fundamentan y sobre qué tipos de problemas pueden aplicarse. El enfoque será teórico-práctico y se hará uso del lenguaje de programación Python y del toolkit Scikit Learn. Se recomienda a los alumnos instalarse ANACONDA en su plataforma habitual. ANACONDA incluye Python, Scikit-Learn y Matplotlib. La versión de python que utilizaremos será la 3.6.También veremos pyspark como plataforma de desarrollo de aplicaciones distribuídasEntre los principales objetivos podemos destacar:Introducir los conceptos de ciencias de datos y machine learning.Introducir las principales librerías que podemos encontrar en python para aplicar técnicas de machine learning a los datos.Introducir las principales librerías que podemos encontrar en python para tratamiento y visualización de datos Dar a conocer los pasos para construir un modelo de machine learning, desde la adquisición de datos,pasando por la generación de funciones, hasta la selección de modelos.Dar a conocer los principales algoritmos para resolver problemas de machine learning.Introducir scikit-learn como herramienta para resolver problemas de machine learning.Introducir pyspark como herramienta para aplicar técnicas de big data y map-reduce a los datos.Conocer y aplicar algoritmos de machine learning con pyspark.Introducir los sistemas de recomendación basados en contenidos
Learn The Complete Artificial Intelligence Course 2025
This specialization from Johns Hopkins University covers how to apply AI techniques to develop practical cybersecurity tools, including machine learning and deep learning models to detect threats.
Sevgili arkadaşlar merhaba,NLP’nin temel kavramlarını ve modern dil modellerinin gelişimini keşfetmeye hazır mısınız? Bu kursta, NLP’nin başlangıcından Büyük Dil Modelleri’nin (LLMs) yükselişine kadar olan süreci adım adım ele alacağız.Kurs, temel bilgilerden karmaşık modellere doğru bir öğrenme deneyimi sunacak şekilde tasarlandı. Amacımız, NLP’nin gelişimini sade ve anlaşılır bir şekilde aktararak, teorik altyapıyı en iyi şekilde kavramanızı sağlamak. NLP’nin evrimindeki her adımı sırasıyla inceleyecek ve bu alandaki kavramları derinlemesine öğreneceksiniz. Eğer bu alanda yeniyseniz veya mevcut bilginizi derinleştirmek istiyorsanız, bu kurs sizin için ideal!Eğitimin içerisinde Doğal Dil İşlemenin evrim basamaklarını aşağıdaki sıra ile öğreneceğiz:KURAL TABANLI NLP (RULE-BASED NLP)İSTATİSTİKSEL NLP (STATISTICAL NLP)FREKANS TABANLI METİN VEKTÖRİZASYONU (FREQUENCY-BASED TEXT VECTORIZATION)BAĞLAMDAN BAĞIMSIZ STATİK TEMSİLLER (CONTEXT-INDEPENDENT STATIC EMBEDDINGS)BAĞLAMLAŞTIRILMIŞ TEMSİLLER (CONTEXTUALIZED EMBEDDINGS)BU KURS SİZE NE KAZANDIRACAK?NLP'nin temel kavramlarından LLM'lere uzanan yolculuğu anlama fırsatı.Büyük Dil Modellerinin (LLMs) temel mantığı, mimarisi ve uygulama alanları hakkında detaylı bilgi.NLP projelerinde kullanabileceğiniz pratik ve teorik bilgi.Güncel NLP araçları ve modelleriyle çalışma becerisi.Kurs; Makine Öğrenimi, Derin Öğrenme, Doğal Dil İşleme ve Büyük Dil Modelleri (LLMs) alanlarında uzmanlaşan Musa Bayır tarafından hazırlanmıştır. Eğer NLP ve Büyük Dil Modelleri (LLMs) hakkında kapsamlı teorik bilgi edinmek ve bu alandaki bilginizi derinleştirmek istiyorsanız hemen kayıt olun.
Do you want to super charge your career by learning the most in demand skills? Are you interested in data science but intimidated from learning by the need to learn a programming language?I can teach you how to solve real data science business problems that clients have paid hundreds of thousands of dollars to solve. I'm not going to turn you into a data scientist; no 2 hour, or even 40 hour online course is able to do that. But this course can teach you skills that you can use to add value and solve business problems from day 1.This course is different than most for several reasons:1. We start with problem solving instead of coding. I feel like starting to code before solving problems is misguided; many students are turned off by hours of work to try to write a couple of meaningless lines rather than solving real problems. The key value add data scientists make is solving problems, not writing something in a language a computer understands.2. The examples are based on real client work. This is not like other classes that use Kaggle data sets for who survived the Titanic, or guessing what type of flower it is based on petal measurements. Those are interesting, but not useful for people wanting to sell more products, or optimize the performance of their teams. These examples are based on real client problems that companies spent big money to hire consultants (me) to solve.3. Visual workflows. KNIME uses a visual workflow similar to what you'll see in Alteryx or Azure Machine Learning Studio and I genuinely think it is the future of data science. It is a better way of visualizing the problem as your are exploring data, cleaning data, and ultimately modeling. It is also something that makes your process far easier to explain to non-data scientists making it easier to work with other parts of your business.Summary: This course covers the full gamut of the machine learning workflow, from data and business u
En este curso veremos cómo implementar nuestros modelos de inteligencia artificial en una aplicación Android utilizando Tensorflow Lite. El tensorFlow lite es un conjunto de herramientas que nos ayuda a ejecutar modelos de TensorFlow en dispositivos móviles, integrados y de IoT. Esta nos permitirá realizar la inferencia en un dispositivo móvil. Implementaremos desde cero un modelo de “Regresión Lineal” en Python y lo llevaremos a Android utilizando Tensorflow Lite. Implementaremos desde cero un modelo de “Regresión en Múltiple” con normalización de datos y lo llevaremos a Android utilizando Tensorflow Lite. Implementaremos desde cero una “Red Neuronal Convolucional” para clasificar imágenes y llevaremos el modelo a Android utilizando Tensorflow Lite. Implementaremos un ejemplo de detección de objetos basado en la “Red Neuronal Convolucional” MobileNet. Implementaremos desde cero una “Red Neuronal Artificial” para clasificar dígitos utilizando el dataset MNIST y llevaremos el modelo a Android para reconocer dígitos del 0 al 9 utilizando Tensorflow Lite. Entrenamiento del algoritmo Yolo en Google Colab y despliegue en Aplicación Android. Veremos también como descargar cientos de imágenes para elaborar datasets de manera automática. Implementaremos la técnica de “Data Augmentation” para incrementar la precisión de nuestros modelos de clasificación de imágenes. Además implementaremos OpenCV para segmentar y reconocer digitos escritos a mano.Los invito cordialmente a tomar el curso en donde aprenderán a implementar sus modelos de inteligencia artificial en una aplicación Android.
This graduate certificate program teaches how to construct Intelligent Systems that utilize computational reasoning to solve problems based on captured domain knowledge and data. The curriculum covers the core concepts and techniques of intelligent reasoning systems and familiarizes students with the best practices and tools for building them.
An online course to promote the responsible use of AI in transportation, equipping practitioners with knowledge of AI technologies and policy considerations to improve safety, mobility, and efficiency.
An eight-week online program that empowers participants to lead the entire product life cycle, from vision and discovery to go-to-market strategy and growth, with an AI-forward curriculum.
A free, self-paced course designed to help educators understand and integrate artificial intelligence into their teaching practices. It covers AI fundamentals, practical classroom applications, and ethical considerations.
This course provides an in-depth understanding of conversational systems from both a theoretical and practical perspective. Topics include natural language processing, speech technology, multi-modal interfaces, and large language models.
Yapay Zeka uygulamaları geliştirmek, Langchain'e hakim olmak, Retrieval Augmented Generation örnekleri geliştirmek ve LLM orkestrasyonu konusunda uzmanlaşmak istiyorsanız şu anda doğru yerdesiniz!Bu eğitim ile Langchain'in nasıl çalıştığını, nasıl kullanıldığını, RAG gibi önemli konseptlerin nasıl uygulandığını örnekler yaparak öğrenceksiniz. Eğitim içerisinde anlatılan tüm teknik konular translator, ileri seviye chatbot, ileri seviye RAG gibi uygulamalar yapılarak pekiştirilmektedir. Profesyonel anlamda ortaya uygulamalar koymak için gerekli olan tüm bilgilerin de üstünden geçeceğimiz bu eğitime katılmak için temel programlama bilgisine sahip olmanız gerekmektedir.Kurs Udemy'de 350.000+ öğrenciye Yazılım ve Siber Güvenlik eğitimleri veren ve Boğaziçi Üniversitesi'nde Yazılım Eğitmeni olan Atıl Samancıoğlu tarafından veriliyor! Siz de Yapay Zeka orkestrasyonuna adım atmak istiyorsanız aşağıdaki detaylı açıklamayı inceleyip kursa hemen kayıt olabilirsiniz.Bu kursta Langchain, RAG vb. birçok konuyu beraber işleyeceğiz. Eğitimin içindeki bölümlerde değineceğimiz konulardan bazıları şunlar:LangchainLanggraphRetrieval Augmented GenerationAgentsMessage History, Memory ManagementLangsmithLangserveVectorstoreChromaDBOpenAI APICustom GPTsİçerik & Genel GörünüşBu kurs yapay zeka araçları geliştirmeyi ve özellikle de büyük dil modellerini bu sürece dahil etmeyi düşünenler için idealdir. LLM'lerin Langchain ile nasıl yönetilebileceğini, Langgraph ile Agent'larımızın nasıl koordine edilebileceğini, RAG gibi ileri seviye konuların nasıl basit hale getirileceğini pratik projeler yaparak göreceğiz.Eğitim pratik odaklı olsa da kesinlikle teorik boyutu aksatılmadan en ince detayları işlemeyi ih
This track covers the fundamental concepts of machine learning, with a focus on supervised learning techniques using Python.
Este curso foi desenvolvido para proporcionar uma experiência prática e profunda no uso de modelos de linguagem, ferramentas avançadas e estratégias de implementação voltadas para a criação de soluções baseadas em inteligência artificial. A seguir, apresentamos os tópicos que serão abordados:Seção 1: OpenAI e Suas Ferramentas PoderosasIntrodução aos Modelos de Linguagem da OpenAI:Compreenda os fundamentos dos modelos como GPT-4, suas capacidades, limitações e potenciais de aplicação.Construindo Chatbots Personalizados:Aprenda a criar chatbots sofisticados e altamente customizáveis para atender às necessidades específicas do usuário.Function Calling:Entenda como utilizar o recurso de function calling para integrar modelos a sistemas externos e realizar chamadas dinâmicas de funções.Explorando Imagens com DALL·E:Descubra como gerar, editar e utilizar imagens criadas com DALL·E em soluções práticas.Trabalhando com Áudio:Explore o potencial das ferramentas de reconhecimento e síntese de voz da OpenAI, como Whisper e outros recursos inovadores.Melhores Práticas de Integração:Saiba como conectar as ferramentas OpenAI de maneira eficiente a outras tecnologias e APIs.Seção 2: Fundamentos e Ferramentas do LangChainIntrodução ao LangChain:Descubra como o LangChain facilita a integração de LLMs em fluxos de trabalho complexos.Modelos (Models):Configure e gerencie LLMs e seus parâmetros para diferentes necessidades.Templates de Prompts:Crie e otimize prompts utilizando as classes de prompt template para maximizar a eficácia do modelo.Análise de Saída (Output Parser):Aprenda a estruturar e interpretar as
This professional certificate course equips professionals with skills to integrate AI with emotional intelligence for personal and organizational growth. It covers empathy mapping, sentiment analysis, and team dynamics.
Este curso abrange uma jornada empolgante na aplicação do ChatGPT no campo da Ciência de Dados e Machine Learning. Ao longo deste programa, você explorará a capacidade do ChatGPT como uma ferramenta valiosa na análise de dados, no pré-processamento e na construção de modelos de aprendizado de máquina sem precisar digitar uma linha sequer de código!Na primeira parte, mergulharemos nas técnicas fundamentais de análise de dados. Você aprenderá como extrair informações estatísticas cruciais de seus conjuntos de dados, lidar com valores ausentes e identificar e tratar valores atípicos (outliers). Exploraremos as relações entre as variáveis e a representação visual de dados categóricos e numéricos. Além disso, você terá a oportunidade de criar gráficos interativos, tornando a exploração de dados mais envolvente e informativa. Na segunda parte, nos aprofundaremos no campo do machine learning e você aprenderá a lidar com atributos categóricos usando técnicas como o LabelEncoder e o OneHotEncoding. Abordaremos o desafio de conjuntos de dados desbalanceados e discutiremos a importância da transformação de escala. Você também ganhará experiência na divisão eficaz de bases de dados, seleção de algoritmos apropriados e métodos de avaliação. A validação cruzada, tuning de parâmetros e seleção de atributos são partes essenciais do processo de modelagem, e você terá a oportunidade de aprimorar suas habilidades nessas áreas.Ao concluir este curso, você estará equipado com habilidades avançadas em ciência de dados e machine learning, capacitado para aplicar o ChatGPT de forma eficaz em projetos do mundo real. Este programa oferece uma oportunidade única de melhorar suas habilidades analíticas e se destacar no campo da ciência de dados e do aprendizado de máquina. Prepare-se para alcançar um novo patamar em sua carreira profissional!
Learn Time Series Analysis and Forecasting with Python
An interactive DataCamp course teaching the fundamentals of causal inference and how to implement various methods in R.
A book that provides a comprehensive guide to machine learning using two popular Python libraries, covering a wide range of supervised learning models.
¡Bienvenidos al curso de ChatGPT en español!ChatGPT se está convirtiendo en una herramienta para la productividad que todos vamos a tener que aprender, así cómo en su momento fue Word o Excel, en este curso te voy a llevar paso a paso por todas las características que cuenta chatGPT. Este curso de chatGPT en español se centra en la productividad, vamos a ver cómo podemos utilizar la herramienta en nuestro día a día, existen varios casos de negocio para poner a prueba los límites de la herramienta.En este curso, no solo aprenderás a usar la herramienta de inteligencia artificial de ChatGPT, sino que también aprenderás a utilizar la técnica avanzada de Prompt Engineering para sacar el mayor provecho de la herramienta y aumentar tu productividad.Durante el curso, te guiaré paso a paso para que puedas entender cómo funciona ChatGPT, cómo utilizar sus funciones y cómo aplicar las técnicas de Prompt Engineering para obtener mejores resultados en tus solicitudes.Una de las ventajas de este curso es que proporcionaré casos de uso específicos para empresarios y estudiantes, para que puedas aplicar fácilmente lo que aprendas en tu vida diaria. Aprenderás cómo utilizar ChatGPT para redactar correos electrónicos, informes, cartas de presentación y cualquier otro tipo de contenido que necesites para tu trabajo o estudio. También te mostraré cómo utilizar ChatGPT para realizar investigaciones y proyectos de una manera más rápida y efectiva.En resumen, si estás interesado en aprender ChatGPT y en aprovechar al máximo sus capacidades, este curso es para ti. Únete hoy y comienza a aprender de una manera emocionante e interactiva para mejorar tu productividad y potenciar tus habilidades. ¡Te espero en clase!
This specialization covers the foundational concepts of data science, including data wrangling and visualization as part of the exploratory data analysis process.
Interested in the field of Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!This course has been designed by a software engineer. I hope with the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.I will walk you step-by-step into Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.This course is fun and exciting, but at the same time, we dive deep into Recurrent Neural Network. Throughout the brand new version of the course, we cover tons of tools and technologies including:Deep Learning.Google ColabKeras.Matplotlib.Splitting Data into Training Set and Test Set. Training Neural Network.Model building.Analyzing Results.Model compilation.Make a Prediction.Testing Accuracy.Confusion Matrix.ROC Curve.Text analysis.Image analysis.Embedding layers.Word embedding.Long short-term memory (LSTM) models.Sequence-to-vector models.Vector-to-sequence models.Bi-directional LSTM.Sequence-to-sequence models.Transforming words into feature vectors.frequency-inverse document frequency.Cleaning text data.Processing documents into tokens.Topic modelling with latent Dirichlet allocationDecomposing text documents with LDA.Autoencoder.Numpy.Pandas.Tensorflow.Sentiment Analysis.Matplotlib.out-of-core learnin
A LinkedIn Learning course that explores the intersection of causal inference and machine learning, teaching how to build more robust and interpretable models.
Prerequisites: No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to run the source code that is provided. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. This course is a down-to-earth, shy but confident take on machine learning techniques that you can put to work today Let’s parse that. The course is down-to-earth : it makes everything as simple as possible - but not simpler The course is shy but confident : It is authoritative, drawn from decades of practical experience -but shies away from needlessly complicating stuff. You can put ML to work today : If Machine Learning is a car, this car will have you driving today. It won't tell you what the carburetor is. The course is very visual : most of the techniques are explained with the help of animations to help you understand better. This course is practical as well : There are hundreds of lines of source code with comments that can be used directly to implement natural language processing and machine learning for text summarization, text classification in Python. The course is also quirky. The examples are irreverent. Lots of little touches: repetition, zooming out so we remember the big picture, active learning with plenty of quizzes. There’s also a peppy soundtrack, and art - all shown by studies to improve cognition and recall. What's Covered: Machine Learning: Supervised/Unsu
This course covers RNNs, LSTMs, and GRUs in Tensorflow. It includes projects on time series prediction, music generation, language translation, image captioning, spam detection, and action recognition.
This course is for solution architects, data professionals, and tech leads looking to integrate AI into their frameworks. It focuses on real-world applications, a 'Data First' approach, and mastering data science techniques for strategic AI success.
This course is designed to upskill national security professionals in AI, focusing on hands-on, use-case driven applications of AI tools like ChatGPT and Gemini for national security missions.
This specialization provides a foundational understanding of how machine learning works, and when and how it can be applied to solve problems. Learners will build skills in applying the data science process and industry best practices to lead machine learning projects, and develop competency in designing human-centered AI products which ensure privacy and ethical standards.
Welcome to the course wine quality prediction! In this course you will learn how to work with data from end-to-end and create a machine learning model that predicts the quality of wines.This data set contains records related to red and white variants of the Portuguese Vinho Verde wine. It contains information from 1599 red wine samples and 4898 white wine samples. Input variables in the data set consist of the type of wine (either red or white wine) and metrics from objective tests (e.g. acidity levels, PH values, ABV, etc.).It is super important to notice that you will need python knowledge to be able to understand this course. You are going to develop everything using Google Colab, so there is no need to download Python or Anaconda. You also need basic knowledge of Machine Learning and data science, but don't worry we will cover the theory and the practical needs to understand how each of the models that we are going to use work.In our case, we will work with a classification problem (a set from the supervised learning algorithms). That means that we will use the quality as the target variable and the other variables as the inputs. In this sense, we will some examples to train our model and predict the quality of other wines.You will learn to work with Decision Trees, Logistic Regression, how to use LazyPredict and how to tune the hyperparameters using Grid Search.
This course focuses on applying time series analysis to real-world business problems. You will work on projects like predicting temperature, COVID-19 cases, and stock prices.
This module helps you understand the different types of AI, where they can be used, and how data impacts each type, enabling you to stay relevant in an AI-focused world.
A warm welcome to the Machine Learning and Data Science Interview Guide course by Cloud Excellence Academy.We provides this unique list of Data Science Interview Questions and Answers to help you prepare for the Data Scientist and Machine Learning Engineer interviews. This exhaustive list of important data science interview questions and answers might play a significant role in your interview preparation career and helping you get your next dream job. The course contains real questions with fully detailed explanations and solutions. Not only is the course designed for candidates to achieve a full understanding of possible interview questions, but also for recruiters to learn about what to look for in each question response. Why Data Science Job ?According to Glassdoor, a career as a Data Scientist is the best job in America! With an average base salary of over $120,000, not only do Data Scientists earn fantastic compensation, but they also get to work on some of the world's most interesting problems! Data Scientist positions are also rated as having some of the best work-life balances by Glassdoor. Companies are in dire need of filling out this unique role, and you can use this course to help you rock your Data Scientist Interview!Let's get started!Unlike others, We offer details explanation to each and every questions that will help you to understand the question100% money back guarantee (Unconditional, we assure that you will be satisfied with our services and be ready to face the data science interview).The Course highlights100 Questions on Machine Learning Algorithms , Use Cases ,Scenarios, Regularizations etc.75 Questions on Deep Learning ( ANN , CNN , RNN , LSTM , Transformer)100 Questions on Statistics and Probability 50 Question on Pyth
Learn how to create a variety of visualizations in Python using Matplotlib and Seaborn to effectively explore and present your data.
Tauche ein in die kreative Seite der Künstlichen Intelligenz – mit Generativen Neuronalen Netzwerken (GANs), Autoencodern und Adversarial Attacks. In diesem praxisorientierten Kurs lernst du, wie du mit Python, TensorFlow 2.14 und Keras eigene Deep-Learning-Modelle entwickelst, trainierst und sogar „hackst“.Nach einer kurzen Einführung in die Grundlagen von Machine Learning und Deep Learning, baust du Schritt für Schritt eigene neuronale Netze auf – von klassischen Deep Neural Networks bis hin zu verschiedenen Arten von GANs. Du verstehst nicht nur, wie diese Modelle funktionieren, sondern setzt sie auch selbst um – mit zahlreichen spannenden Coding-Sessions.Neben der Generierung realistischer Daten mit Variational Autoencodern (VAE) und der Datenkomprimierung mit klassischen Autoencodern, wirst du auch lernen, wie neuronale Netze durch gezielte Adversarial Attacks ausgetrickst werden können – und wie man sich dagegen schützt.Dieser Kurs richtet sich an alle, die ein solides Verständnis im Deep Learning aufbauen und moderne generative Modelle praktisch umsetzen möchten. Egal ob Data Science Student, KI-Enthusiast oder Entwickler – hier wirst du gefordert und gefördert.Das wirst du lernen:Grundlagen von Machine Learning & Deep LearningEigene Deep Neural Networks mit TensorFlow & Keras entwickelnAdversarial Generative Networks (GANs) verstehen und implementierenAdversarial Attacks: Netzwerke gezielt angreifen & absichernDaten komprimieren mit Autoencodern (AE)Realistische Daten generieren mit Variational Autoencodern (VAE)Arbeiten in Python (über Anaconda oder andere Installationen)Werde jetzt Teil der KI-Zukunft – mit deinem eigenen generativen Netzwerk.Let’s code the future – wir sehen uns im Kurs!Hinweis:Python wird im Kurs mit Anac
In this course, we will implement a neural network from scratch, without dedicated libraries. Although we will use the python programming language, at the end of this course, you will be able to implement a neural network in any programming language. We will see how neural networks work intuitively, and then mathematically. We will also see some important tricks, which allow stabilizing the training of neural networks (log-sum-exp trick), and to prevent the memory used during training from growing exponentially (jacobian-vector product). Without these tricks, most neural networks could not be trained. We will train our neural networks on real image classification and regression problems. To do so, we will implement different cost functions, as well as several activation functions. This course is aimed at developers who would like to implement a neural network from scratch as well as those who want to understand how a neural network works from A to Z. This course is taught using the Python programming language and requires basic programming skills. If you do not have the required background, I recommend that you brush up on your programming skills by taking a crash course in programming. It is also recommended that you have some knowledge of Algebra and Analysis to get the most out of this course. Concepts covered : Neural networks Implementing neural networks from scratch Gradient descent and Jacobian matrix The creation of Modules that can be nested in order to create a complex neural architecture The log-sum-exp trick Jacobian vector product Activation functions (ReLU, Softmax, LogSoftmax, ...) Cost functions (MSELoss, NLLLoss, ...) This course will be frequently updated, with the addition of bonuses. Don't wait any longer before launching yourself i
An in-depth program covering data profiling techniques, data quality assessment, and data cleansing. Learners will gain a comprehensive understanding of how to analyze and improve data quality for various applications.
A área de Machine Learning (Aprendizagem de Máquina) e Data Science (Ciência de Dados) é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo responsável pela utilização de algoritmos inteligentes que tem a função de fazer com que os computadores aprendam por meio de bases de dados. O mercado de trabalho de Machine Learning nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação! E dentro deste contexto está o cientista de dados, que já foi classificado como o trabalho "número 1" por vários veículos da mídia internacional.E para levar você até essa área, neste curso completo você terá uma visão teórica e prática sobre os principais algoritmos de machine learning utilizando o Python, que é uma das linguagens de programação mais relevantes nesta área. Além disso, vamos utilizar o Google Colab para a implementação dos exemplos, o que facilita o entendimento dos conceitos e evita problemas de instalação de bibliotecas. Este curso é considerado de A à Z pelo fato de apresentar desde os conceitos mais básicos até técnicas mais avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Você aprenderá tudo passo a passo, ou seja, tanto a teoria quanto a prática de cada algoritmo! O curso é dividido em cinco partes principais:Classificação - pré-processamento dos dados, naïve bayes, árvores de decisão, random forest, regras, regressão logística, máquinas de vetores de suporte (SVM), redes neurais artificiais, avaliação de algoritmos e combinação e rejeição de classificadoresRegressão - regressão linear simples e múltipla, polinomial, árvores de dec
This specialization from Vanderbilt University teaches how to leverage ChatGPT's free AI tools for various professional tasks including planning, project management, writing, data analytics, and marketing. It also covers Anthropic Claude.
This specialization will help you build a strong foundation in how machines perceive and analyze visual information. You will discover how transformers, Vision Transformers (ViT), CLIP, and diffusion models are reshaping the future of AI.
A certification for engineers who build, manage, and deploy AI solutions using Azure AI services. Responsibilities include participating in all phases of AI solution development, from requirements and design to deployment and maintenance.
This course covers a wide range of machine learning algorithms in R, including a dedicated section on tree-based methods.
A Wesleyan University specialization that teaches how to analyze and interpret data, with a focus on statistical methods and their application in various fields.
This 5-course specialization is intended for professionals seeking to develop a skill set for interpreting statistical results. You will cover descriptive statistics, data visualization, measurement, regression modeling, probability and uncertainty to prepare you to interpret and critically evaluate a quantitative analysis.
This specialization demonstrates how to use Excel for data analysis and visualization, which can be a powerful tool for initial data exploration.
An introductory course to Power BI, a popular tool for creating interactive dashboards and visualizations for exploratory data analysis.
You don’t want to code, but you do want to know about Big Data, Artificial Intelligence and Machine Learning? Then this course is for you!You do want to code and you do want to learn more about Machine Learning, but you don’t know how to start? Then this course is for you!The goal of this course is to get you as smoothly as possible into the World of Machine Learning. All the buzzwords will now be clear to you. No more confusion about “What’s the difference between Machine Learning and Artificial Intelligence.” No more stress about “This is just too much information. I don’t know where to start”The topics in this course will make it all clear to you. They are :Part 1 - WelcomePart 2 - Why machine learning?Part 3 - BuzzwordsPart 4 - The Machine Learning ProcessPart 5 - ConclusionBut it does not have to end here. As a bonus, this course includes references to the courses which I find the most interesting. As well as other resources to get you going.
A seven-course specialization from IBM that covers the fundamentals of generative AI, the structure of large language models, and fine-tuning methods. The program is estimated to take 3-6 months to complete at 4 hours per week and has a 4.5-star rating with about 9,000 active learners.
This specialization, offered by IBM, is designed for project managers, scrum masters, and coordinators looking to integrate generative AI into their workflows. It covers prompt engineering, AI-driven project tools, and how to use AI to improve project documentation and performance. The course is self-paced and aims to help professionals drive efficiency and report a positive ROI from AI in project management.
This Johns Hopkins University specialization provides a comprehensive overview of the entire data science pipeline, including statistical modeling and machine learning algorithms.
This specialization is aimed at leaders and consultants, exploring generative AI applications across business domains and their integration into operations, while considering data privacy and ethical implications.
A specialization that provides the foundational SQL skills needed to query and extract data for exploratory data analysis.
A specialization from Duke University that covers a range of topics from generative AI techniques to managing open source LLMs on various platforms like Azure, AWS, and Databricks. It provides hands-on experience in designing, deploying, and scaling language models.
This specialization from Google Cloud on Coursera teaches how to build and deploy ML models on Google Cloud Platform. It covers Vertex AI, AutoML, BigQuery ML, and TensorFlow, preparing learners for a career in cloud-based machine learning.
This specialization covers the essential mathematical foundations for machine learning, including linear algebra, multivariate calculus, and principal component analysis (PCA). It's designed to provide the necessary mathematical background for a career in ML.
This specialization from DeepLearning.AI provides a foundational understanding of the mathematics essential for AI and machine learning. It covers linear algebra, calculus, probability, and statistics, with a focus on their application in data science. Learners will gain skills in statistical hypothesis testing, Bayesian statistics, and exploratory data analysis.
This Specialization provides a rigorous treatment of robotics, covering mechanics, planning, and control. It is intended for students with a desire to learn the foundational principles of modern robotics.
Dans ce cours, vous allez découvrir et approfondir les différents aspects liés à l'apprentissage automatique avec Python. Nous utiliserons les librairies telles que Tensorflow, Keras, Pandas, Numpy, Scikit learn, ...Les travaux sont accessibles et exploitables en ligne grâce à l'utilisation des carnets Jupyter avec Google Colab. Aucune installation de logiciel spécifique sur son ordinateur n'est requise car tout le travail se fait en ligne.A chaque étape d'apprentissage de ce cours, de nouveaux modèles sont introduits. Des explications claires permettent de bien les comprendre à travers 6 thèmes d'étude :Structure de base d'un réseau de neuronesReconnaissance d'image avec un réseau de neurones à convolution 2DTraitement d'image avec un réseau de neurones profond à convolution 2DSystèmes de recommandations et d'analyse des ressentisDétection d'anomalies dans les donnéesAnalyse et prédiction sur les séries temporellesLes activités en Python expliquent clairement comment les exploiter. Des exercices sont régulièrement proposés pour consolider votre apprentissage.D'une durée totale de 19,5 heures, ce cours vous permettra d'être à l'aise avec les outils actuels du Deep Learning. Vous serez alors capable d'utiliser ces ressources pour créer vos propres projets et d'approfondir avec sérénité et en autonomie vos connaissances dans ce domaine.=== Prérequis ===Vous n'avez pas besoin d'être un spécialiste du langage Python. En effet, au fur et à mesure de votre progression, vous manierez ce langage et découvrirez les subtilités liées à son utilisation.Si vous êtes complètement débutant en Deep Learning, alors ce cours est fait pour vous. Ce cours est structuré de manière progressive pour acquérir petit à petit les bases du de
This course explores the intersection of generative AI and blockchain technology, examining how they can mutually enhance each other to create innovative solutions across various industries. It covers how blockchain can provide a secure and transparent infrastructure for data integrity, enable trusted federated learning, and improve AI decision-making.
A comprehensive specialization from UC Davis that covers the theory behind search engine algorithms and practical skills for optimizing website content. It includes modules on on-page and off-page optimization, keyword research, and aligning SEO with business strategies.
A three-course specialization from the University of Michigan that teaches beginning and intermediate concepts of statistical analysis using Python, covering data design, exploration, and modeling.
This specialization from Duke University teaches you how to analyze and visualize data in R. It covers topics such as probability, inference, regression, and machine learning. The specialization is very hands-on and includes several projects.
A free online learning program that covers what AI is, its different types and uses, ethical considerations, and its applications in business and environmental sustainability. The 10-hour course allows participants to learn at their own pace and receive a co-branded certificate upon completion.
A comprehensive course on leveraging Generative AI in marketing and sales. It includes modules on forecasting trends, analyzing sales data, and a specific section on Customer Lifetime Value Prediction with GenAI.
En este curso se enseñan todos los conocimientos necesarios para convertirse en un Data Scientist (Científico de Datos). Para ello usaremos el lenguaje de Programación Python como herramienta, ya que es uno de los lenguajes con más demanda hoy en dia.En concreto, se tratarán en profundidad los siguientes apartados:- Programación en Python, donde aprendemos a programar en uno de los lenguajes más populares hoy en día como es Python.- Análisis de Datos, donde aprenderemos como realizar un Análisis Exploratorio de Datos, usando técnicas estadísticas y de Visualización de Datos.- Machine Learning, donde aprenderemos como crear modelos predictivos, evaluarlos y usarlos en un entorno de desarrollo.- Deep Learning, donde nos enfocamos en la creación de Redes Neuronales.- Web Scraping, donde aprenderemos técnicas para extraer información de páginas web.- Big Data, donde aprenderemos a como procesar datasets de gran tamaño asi como entrenar modelos predictivos con ellos.
This course equips you with the skills to build real-world NLP applications using transformer models from the Hugging Face ecosystem. You will gain hands-on experience with speech-to-text pipelines, sentiment analysis, and text generation.
Explore diverse perspectives on AI's impact on education through exclusive interviews with thought leaders discussing transformation in learning, future skills, and adaptation strategies for educators.
An intermediate-level course that introduces an important class of statistical models. It covers the basic concepts of mixture models, Bayesian estimation for these models, and their applications in density estimation and clustering.
This course introduces the fundamental concepts of parallel programming using CUDA. Students will learn about thread management, memory types, and performance optimization techniques for solving complex problems on Nvidia hardware.
This article-based course explores the regulations, guidelines, and frameworks that govern the use of AI technologies across various sectors, addressing issues like data privacy and ethical considerations.
Fortinet offers a free curriculum of courses covering security-driven networking, adaptive cloud security, AI-driven security operations, and zero-trust network access.
Part of the Competitive Strategy and Organization Design Specialization, this course delves into how companies can build and maintain their customer base. Topics include increasing switching costs, strategic customer lock-ins, price discrimination, and product differentiation strategies. It will equip you with skills in competitive analysis, market analysis, and business strategy.
This course covers the LLMOps pipeline, including pre-processing training data for supervised instruction tuning, and adapting a supervised tuning pipeline to train and deploy a custom LLM. You'll learn best practices like versioning your data and models, and pre-processing large datasets. The course also touches on responsible AI by outputting safety scores.
A search result page on Coursera for courses related to optimization algorithms, offering a variety of options.
An 8-course professional certificate series by IBM that teaches you to build next-generation AI systems using Retrieval-Augmented Generation and Agentic AI. You'll gain skills in LangChain, OpenAI, and responsible AI.
This course teaches how to transform unstructured documents into actionable data using Google's Document AI platform. It covers using Python, OCR, and form parsing techniques to master automated data extraction pipelines through hands-on labs.
A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina).A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre as principais e mais modernas técnicas de Deep Learning utilizando a biblioteca PyTorch o Python! Este curso apresenta desde os conceitos mais básicos sobre as redes neurais até técnicas mais modernas e avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Para isso, o conteúdo está dividido em sete partes: redes neurais artificiais, redes neurais convolucionais, autoencoders, redes adversariais generativas (GANs)</strong
This specialization helps learners understand AI as a process of intelligent decision-making to solve challenges in health systems and apply AI solutions responsibly.
This professional certificate program equips you with the skills to launch software products using Microsoft tools like Copilot and Power BI. It covers B2B market research, cloud strategy, product launch, and stakeholder management, with a specific course on 'Product Strategy and Roadmapping'.
A comprehensive online program for data engineers and practitioners. This certificate equips you with the skills and knowledge to excel in a high-demand field, focusing on ingesting, processing, transforming, storing, and serving data for data science and machine learning use cases. You'll learn the foundations of data engineering while gaining hands-on experience designing and implementing data architectures using AWS and open-source tools.
This course explores the application of Artificial Intelligence in the context of autonomous vehicles and robotics, covering key concepts and techniques in this rapidly growing field.
A two-part course series that teaches how to build a federated learning system using the Flower framework. The first part covers the federated training process, customization, and privacy-enhancing techniques. The second part focuses on applying federated learning to Large Language Models (LLMs) with private data.
This specialization is designed for consultants to leverage Generative AI tools effectively. It covers the application of Gen AI across industries to address complex challenges and deliver value to clients through innovative approaches, with a focus on responsible and ethical use.
This course explores the application of Artificial Intelligence in various HR functions. It covers how AI is used in talent acquisition, including resume screening and candidate assessment, which often involves personality and skill evaluation. The curriculum delves into the use of AI for improving recruitment efficiency and making data-driven decisions in hiring.
This course focuses on the theory and practice of various classification algorithms in machine learning.
Explore supervised machine learning algorithms, prediction tasks, and model selection. Learn to improve performance using linear/logistic regression, KNN, decision trees, ensembling methods, and kernel techniques like SVM.
An innovative program designed to empower leaders with the skills to harness the full potential of large language models like ChatGPT, revolutionizing leadership strategies and productivity in business and personal life.
This course focuses on the intricacies of data contracts and serialization in Kafka. It explores how serialization enhances Kafka's architecture and examines different serialization formats like AVRO, Protobuf, and Thrift to understand their schema compatibility and applications. The course is designed for software developers and data engineers with basic knowledge of Java and Kafka.
This course covers emerging technologies in real estate, including AI and machine learning, and their applications in areas like market analysis, fraud detection, and improving customer experience.
This course focuses on the core concepts behind neural language models and machine translation, covering RNNs, attention, and transformers. Students learn to build, fine-tune, and evaluate neural models for language understanding and multilingual translation.
An intermediate-level course that introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. It covers basic statistics of data sets, such as mean values and variances and the computation of distances and angles between vectors using inner products.
This course includes a dedicated module on Data QA & Profiling. It covers techniques for univariate and multivariate profiling, common data quality issues like missing values, and data visualization for profiling.
This course explores the transformative power of Generative Artificial Intelligence (GenAI) in mobile app development. It is designed for both experienced mobile app developers and newcomers to the field. Participants will learn how GenAI can optimize efficiency in mobile app testing, user interface optimization, performance analysis, and automation.
Part of the Microsoft AI Product Manager Professional Certificate, this course focuses on gathering and interpreting market data, assessing the competitive landscape, and identifying market opportunities using Microsoft tools. It covers B2B market research, consumer behavior, and competitive analysis techniques.
This course explores the use of computer simulations, particularly agent-based models, to study social science theories. You will learn how to grow and study artificial societies to understand and improve the real world.
This course explores supervised machine learning techniques using the python scikit learn (sklearn) toolkit and real-world athletic data to understand both machine learning algorithms and how to predict athletic outcomes.
Learn to formulate and solve various optimization models that are central to machine learning algorithms.
A self-paced lab in the Google Cloud console where you learn to create and use document processors with the Document AI API. Skills practiced include API usage, testing tools, and Python programming.
This course teaches how to apply knowledge of classification models and embeddings to build a machine learning pipeline that functions as a recommendation engine using TensorFlow on Google Cloud Platform.
This program prepares you for a career as an AI Product Manager, covering key skills like stakeholder engagement, Agile methodologies, and AI fundamentals. A dedicated module, 'Product Management: Foundations & Stakeholder Collaboration', focuses on communication and collaboration skills.
A project-based course where you will learn to build and evaluate classification trees using Python.
This project-based course teaches how to train several classification algorithms like Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier to predict the churn rate of Telecommunication Customers.
A comprehensive article that explains the concepts of feature engineering and selection, and provides methods for handling missing data, continuous features, and categorical features, along with different feature selection techniques.
This specialization, offered by Stanford University, covers the current and future applications of AI in healthcare, aiming to equip learners with the knowledge to bring AI technologies into clinical practice safely and ethically. It is designed for both healthcare and computer science professionals to foster collaboration. The series includes a capstone project with a hands-on experience following a patient's journey.
A specialization that teaches how to create effective data visualizations and dashboards using Tableau, a key skill for exploratory data analysis.
This program focuses on designing, planning, and operating intelligent and integrated energy systems. It covers the integration of renewable energy sources, energy storage, electric vehicles, and the use of AI and machine learning for digitalization and optimization of the grid.
A comprehensive data science program that covers R programming, data visualization, probability, inference, and machine learning. The machine learning section includes classification algorithms and case studies.
This professional certificate program is designed for a broad audience and focuses on the practical application of AI in organizations. It covers various AI solutions like machine learning and natural language processing, providing context for where few-shot and zero-shot learning can be applied.
Learn to use Power Query in Excel for data extraction, transformation, and loading, which are essential skills for data cleaning.
An introductory online course that guides government professionals through designing AI-based services for public administration, emphasizing technical, governance, ethical, and regulatory aspects.
A Harvard University course that teaches the use of causal diagrams (DAGs) to represent assumptions, understand biases, and guide data analysis for causal inference.
An introductory course that covers the data science process, including data acquisition, cleaning, and transformation, using tools like R and Python.
This HarvardX course covers central concepts of statistical inference and modeling, including how to perform inference on high-dimensional data.
Part of Harvard's Data Science Professional Certificate, this course covers the fundamentals of probability theory needed for a data science career.
This course from Microsoft on edX covers the essential mathematical foundations for machine learning and AI using Python.
This MIT course develops a deep understanding of the principles of statistical inference, including estimation, hypothesis testing, and prediction, on firm mathematical grounds.
Learn Complete Python Pandas Data Science Course
Learn about various optimization algorithms and their applications in machine learning and data analysis.
An MIT course that provides a foundational understanding of probability models, including random processes and the basic elements of statistical inference.
A Harvard University course that covers statistical concepts and models relevant for causal inference in the context of high-throughput experiments.
This course from the University of Edinburgh introduces the fundamental concepts of statistics. You will learn about data collection, analysis, and interpretation. The course covers topics such as descriptive statistics, probability, and inference.
This course covers the fundamentals of unsupervised learning techniques such as clustering and dimensionality reduction to make sense of large, unlabeled datasets. You will learn to implement k-means and hierarchical clustering.
Der Kurs ist ein Einsteiger-Kurs in die Welt des Data Science, des Machine Learning, der künstlichen Intelligenz und dem Arbeiten mit Daten. In Zeiten der Digitalisierung und der digitalen Transformation stellt die Wissenschaft der Daten (Data Science) immer mehr eine zentrale Disziplin dar. Ohne grundlegende Kenntnisse und Qualifikationen im Bereich der Daten sind viele Arbeitsplätze kaum noch denkbar.Der Kurs liefert daher einen unkomplizierten Einstieg in die Welt der Daten und der Algorithmen. Dadurch ergibt sich ein Grundverständnis, was Daten überhaupt sind und man sie einer automatischen Verarbeitung mit Algorithmen zugänglich machen kann. Alle Algorithmen und mathematischen Verfahren werden Schritt für Schritt erklärt.Der Lernpfad dieses Kurses besteht u.a. aus folgenden Abschnitten:- Was sind Daten?- Datentypen, Data Mining und Visualisierung von Daten- Statistische Grundbegriffe- Einfache Clustering-Verfahren- Lineare und logistische Regression- Kurze Einführung in die Graphentheorie- Entscheidungsbäume und Random Forest- Einführung in die neuronalen Netze- Überblick über generative KI und deren AnwendungenAlle Algorithmen und Verfahren werden so ausführlich erläutert, dass keine speziellen mathematischen Vorkenntnisse oder IT-Fähigkeiten erforderlich sind. Ein grundlegendes Interesse an mathematischen Zusammenhängen wird hingegen vorausgesetzt. Die Beispiele stehen im Quellcode in der Programmiersprache Python zum Download und zum selber ausprobieren bereit.Der Kurs richtet sich insbesondere an Fach- und Führungskräfte, die selbst mit Daten arbeiten und sich ein tieferes Verständnis grundlegender Zusammenhänge erarbeiten möchten.
A área de Machine Learning (Aprendizagem de Máquina) é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo responsável pela utilização de algoritmos inteligentes que tem a função de fazer com que os computadores aprendam por meio de bases de dados. O mercado de trabalho de Machine Learning nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação! E dentro deste contexto está o cientista de dados, que já foi classificado como o trabalho "número 1" por vários veículos da mídia internacional.E para levar você até essa área, neste curso completo você terá uma visão teórica e prática sobre os principais algoritmos de machine learning utilizando a ferramenta Weka, que é uma das ferramentas mais utilizadas para machine learning e mineração de dados. Além disso, também utilizaremos a linguagem de programação Java para fazer a integração com o Weka! Este curso apresenta desde os conceitos mais básicos até técnicas mais avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Você aprenderá tudo passo a passo, ou seja, tanto a teoria quanto a prática de cada algoritmos! O curso é dividido em cinco partes:Classificação - extração de características de imagens, naive bayes, árvores de decisão, random forest, regras, regressão logística, máquinas de vetores de suporte (SVM), redes neurais artificiais, avaliação de algoritmos e combinação e rejeição de classificadoresRegressão - regressão linear simples e múltipla, polinomial, árvores de decisão, random forest, vetores de suporte e redes neurais artificiaisRegras de associação - algoritmo aprio
Comprehensive lecture notes on stochastic optimization from a leading researcher in the field.
A área de Machine Learning (Aprendizagem de Máquina) é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo responsável pela utilização de algoritmos inteligentes que tem a função de fazer com que os computadores aprendam por meio de bases de dados. O mercado de trabalho de Machine Learning nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação! E dentro deste contexto está o cientista de dados, que já foi classificado como o trabalho "número 1" por vários veículos da mídia internacional.E para levar você até essa área, neste curso completo você terá uma visão teórica e prática sobre os principais algoritmos de machine learning utilizando o R, que é uma das linguagens de programação mais relevantes nesta área de ciência de dados. Este curso é considerado de A à Z pelo fato de apresentar desde os conceitos mais básicos até técnicas mais avançadas, de modo que ao final você terá todas as ferramentas necessárias para construir soluções complexas e que podem ser aplicadas em problemas do dia-a-dia das empresas! Você aprenderá tudo passo a passo, ou seja, tanto a teoria quanto a prática de cada algoritmos! O curso é dividido em cinco partes principais:Classificação - pré-processamento dos dados, naive bayes, árvores de decisão, random forest, regras, regressão logística, máquinas de vetores de suporte (SVM), redes neurais artificiais, avaliação de algoritmos e combinação e rejeição de classificadoresRegressão - regressão linear simples e múltipla, polinomial, árvores de decisão, random forest, vetores de suporte (SVR) e redes neurais artificiaisRegras de associação - algoritmos apriori e ECLATAgrupamento - k-means, agrupamento hierárqu
Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Probability is one of the most important math prerequisites for data science and machine learning. It's required to understand essentially everything we do, from the latest LLMs like ChatGPT, to diffusion models like Stable Diffusion and Midjourney, to statistics (what I like to call "probability part 2").Markov chains, an important concept in probability, form the basis of popular models like the Hidden Markov Model (with applications in speech recognition, DNA analysis, and stock trading) and the Markov Decision Process or MDP (the basis for Reinforcement Learning).Machine learning (statistical learning) itself has a probabilistic foundation. Specific models, like Linear Regression, K-Means Clustering, Principal Components Analysis, and Neural Networks, all make use of probability.In short, probability cannot be avoided!If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know probability.This course will cover everything that you'd learn (and maybe a bit more) in an undergraduate-level probability class. This includes random variables and random vectors, discrete and continuous probability distributions, functions of random variables, multivariate distributions, expectation, generating functions, the law of large numbers, and the central limit theorem.Most important theorems will be derived from scratch. Don't worry, as long as you meet the prerequisites, they won't be difficult to understand. This will ensure you have the strongest foundation possible in this subject. No more memorizing "rules" only to apply them incorrectly / inappropriately in the future! This course will provide you with
Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Calculus is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science. Backpropagation, the learning algorithm behind deep learning and neural networks, is really just calculus with a fancy name.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know calculus.Normally, calculus is split into 3 courses, which takes about 1.5 years to complete.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of years.This course will cover Calculus 1 (limits, derivatives, and the most important derivative rules), Calculus 2 (integration), and Calculus 3 (vector calculus). It will even include machine learning-focused material you wouldn't normally see in a regular college course. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of calculus, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can start applying them today.Are you ready?Let's go!Suggested prerequisites:Firm understanding of high school math (functions, algebra, trigonometry)
A-Z™ | Tensorflow ile Derin ÖğrenmeKursumuzda klasik ve derin öğrenme tabanlı yöntemlerini kullanarak sınıflandırma nasıl yapıldığını öğrenip, Tensorflow kütüphaneleriyle gerçek hayat projeleri yapacağız.Projelerle Yapay Zeka ve Bilgisayarlı Görü Kursu İçeriğiGiriş BölümüDerin Öğrenme TeoriDerin Öğrenme NedirYapay Sinir AğlarıAktivasyon FonksiyonlarıOptimizasyon AlgoritmalarıLoss (Kayıp) FonksiyonlarıDerin Öğrenme TeoriCNN (Convolutional Neural Networks) TeoriEvrişim İşlemiCNN (Convolutional Neural Networks)Piksel Ekleme (Padding)Adım Kaydırma (Stride)Ortaklama (Pooling)Ek Teori Epoch ve Batch SizeDropoutEarly StoppingLearning RateTensorflow ile Derin ÖğrenmeTensorflow TemelleriVeriyi HazırlamaModel Oluşumu SequentialModel EgitimiModel Testi | 1. KısımModel Testi | 2. KısımModeli Kaydetme/Yükleme - Save/LoadModel Sonuçlarını GörselleştirmeModelin Ara Katmalarını GörselleştirmeFunctional Bir Model OluşturmaCallbacks | 1. kısımCallbacks | 2. kısımData Augmentation - Veri Arttırma | 1. KısımData Augmentation - Veri Arttırma | 2. KısımTransfer Learning - VGGHazır Model Kullanma - VGGTensorflow ile Trafik İşaretlerini SınıflandırmaVeriyi HazırlamaModel Eğitimi ve TestReal Time'da TestTensorflow'da Weights & Biases (WandB) | Özel VeriWandb ile Keras'da Temel FonsiyonlarWandb ile Keras'da SweeplerWandb ile Keras'da Sweep - Bonus VideoTensorflow Lite - Android App - Object detection - İmage ClassificationEfficientDet Lite Model Eğitimi - Object detectionEfficientDet Lite Modeli Android'de Çalıştırma 1 - Object detectionEfficientDet Lite Modeli Androi
In this training programme, you will learn Data Science and Machine Learning using Python & R. It will prepare students of any discipline to find lucrative jobs in the vast field of Data Science. Students will also learn Python and R in the process. Data Science is all about processing data received from various sources and deriving information and knowledge from that. This field uses statistics and machine learning tools. Applications are Market analysis, Predictive analytics, Demand Forecast, Recommender Systems, Social Media Analysis, Person analysis etc.
This course from Harvard University explores the concepts and algorithms at the foundation of modern artificial intelligence. It delves into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. The course covers topics including graph search algorithms, reinforcement learning, and neural networks.
A course that explores the transformative potential of AI in educational contexts. It covers the fundamentals of AI, its applications in teaching and learning, and the ethical considerations that educators need to be aware of.
A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, eBay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre as principais e mais modernas técnicas de Deep Learning utilizando o Tensor
Questo corso sul Data Science con R nasce per essere un percorso completo su come si è evoluta l'analisi dati negli ultimi anni a partire dall'algebra e dalla statistica classiche. L'obiettivo è accompagnare uno studente che ha qualche base di R in un percorso attraverso le varie anime del Data Science.Cominceremo con un ripasso delle basi di R, a partire dallo scaricamento e installazione, all'impostazione dell'ambiente di lavoro, passando per le strutture, la creazione di funzioni, l'uso degli operatori e di alcune funzioni importanti. Passeremo poi a vedere come manipolare e gestire un dataset, estrarne dei casi oppure delle variabili, generare dei dataset casuali, calcolare delle misure statistiche di base, creare grafici con i pacchetti Matplotlib e Seaborn.Nelle sezioni successive cominciamo a entrare nel cuore del Data Science con R, a cominciare dal preprocessing: vediamo infatti come ripulire e normalizzare un dataset, e come gestire i dati mancanti. La sezione successiva ci permette di cominciare a impostare dei modelli di machine learning con Python: vedremo tutti gli algoritmi più comuni, sia supervisionati che non supervisionati, come la regressione, semplice, multipla e logistica, il k-nearest neighbors, il Support Vector Machines, il Naive Bayes, gli alberi di decisione e il clustering. Passeremo poi ai più comuni metodi ensemble, come il Random Forest, il Bagging e il Boosting, e all'analisi del linguaggio naturale e al suo utilizzo nel machine learning per la catalogazione dei testi.Nelle ultime sezioni vedremo alcuni rudimenti di analisi temporale, sistemi di raccomandazione e social media mining.
¿Por qué estudiar ciencia de datos?Las empresas tienen un problema: recolectan y guardan enormes cantidades de datos en su día a día. El problema es que no tienen las herramientas y capacidades para extraer conocimiento y tomar decisiones a partir de esos datos. Pero eso está cambiando. Desde hace algunos años, la demanda de científicos de datos ha crecido exponencialmente. Tanto es así, que el número de personas con estas habilidades no es suficiente para cubrir todas las vacantes que hay. Una búsqueda básica en Glassdoor o Indeed te revelará por qué los salarios de los científicos de datos han crecido tanto en los últimos años.¿Por qué este curso?Casi todos los cursos que existen son demasiado teóricos o demasiado prácticos. Los cursos de universidad no suelen desarrollar las habilidades necesarias para enfrentarse a problemas de ciencia de datos desde cero, ni te enseñan a utilizar el software necesario de forma fluida. Por otra parte, muchos cursos y bootcamps online enseñan a utilizar estas técnicas sin llegar a entenderlas en profundidad, pasando por la teoría de forma superficial.Nuestro curso combina lo mejor de cada método. Por una parte, veremos de dónde surgen y por qué se utilizan estos métodos, entendiendo por qué funcionan de la forma que lo hacen. Por otra, vamos a programar estos métodos desde cero, utilizando las librerías más populares de la ciencia de datos y el machine learning en Python. Solo cuando hayas entendido exactamente cómo funciona cada algoritmo, aprenderemos a usarlos con las librerías avanzadas de Python.Contenido del cursoIntroducción al machine learning y a la ciencia de datos.Regresión lineal simple. Aprenderemos a estudiar la relación entre distintos fenómenos.Regresión lineal multiple. Crearemos modelos de más de una variable para estudiar el comportamiento de una variable de interés.Regresión
"Python TOTAL", el curso Best-Seller que ha enseñado Python desde cero a miles y miles, necesitaba un complemento perfecto: "Python TOTAL para Data Science y Machine Learning".¿Por qué hacía falta?Porque con este curso, además de aprender Python desde cero, podrás llevarlo hacia la ciencia del momento: Data Science (o Ciencias de la Información), para poder programar herramientas capaces de procesar cantidades monumentales de información, y de generar no solo visualizaciones relevantes, informativas y atractivas, sino también predicciones a partir de los datos que disponemos.Con "Python Total para Data science & Machine Learning" podrás ayudar a quienes toman decisiones a entender mejor el contexto y la realidad sobre la cual están operando, para poder ser eficaces, eficientes y acertivos en sus decisiones.¿Que encontrarás en este curso?18 días de aprendizaje intenso y prácticoCientos de ejercicios de código en la plataforma (3 por cada lección)Vientos de archivos de código descargableProyectos díarios del mundo real para aplicar lo aprendidoDecenas de bases de datos para prácticasCuestionariosLecciones teóricas y prácticas hechas con amor por la simplicidad¿Qué temas cubre este curso?Python básicoPandasNumPyMatplotlibSeabornScikit LearnTensorflowMachine LearningExcel y Power BI para Data ScienceAlgoritmos de Aprendizaje Supervisado, No Supervisado y por ReforzamientoBases de DatosAPIsDeep LearningEtica y Provacidad en Data Sciencey muchísimo más<
Fai un passo verso il futuro: AI, Machine Learning e Data Science.Sai cosa accomuna il successo dei più grandi colossi del web come Google, Amazon e Facebook ? L'utilizzo che hanno fatto del machine learning.Il machine learning è la branca dell'intelligenza artificiale che ha lo scopo di insegnare ai computer ad apprendere autonomamente, senza essere esplicitamente programmati.Il machine learning non è una novità, ma è finito sotto la luce dei riflettori solo con il nuovo millennio, per due motivi:L'enorme quantità di dati oggi disponibile sul web.Il progresso della tecnologia e il crescente aumento della potenza di calcolo.Questi due fattori, uniti alle sue innumerevoli applicazioni commerciali, stanno contribuendo alla crescita vertiginosa del machine learning che sta trascinando con se l'intero campo dell'intelligenza artificiale.In questo corso pratico imparerai come funziona il machine learning e come utilizzarlo in maniera pratica, utilizzando il linguaggio Python e librerie popolari come Scikit-learn, Pandas e PyPlot.Vuoi dare una svolta alla tua carriera ?L'esperto di machine learning è la professione del futuro e Linkedin lo conferma; secondo una loro recente ricerca il Machine Learning Engineer è la nuova figura più ricercata dalle aziende con un tasso di crescita di quasi il 1000% negli ultimi 5 anni ed è subito seguito dal Data Scientist.Al termine di questo corso avrai acquisito l'esperienza pratica e le intuizioni teoriche necessarie per lanciare la tua carriera in entrambe queste due nuove professioni.Vuoi fondare la tua startup nel campo dell'AI ?Il valore totale del mercato dell'intelligenza artificiale nel 2016 era di 1.3 miliardi di dollari; secondo una ricerca di un'importante società di analisi americana il suo valore per il 2025 potrebbe superare il 60 miliard
Hello there,Welcome to Python Numpy: Machine Learning & Data Science CoursePython numpy, Numpy python, python numpy: machine learning & data science, python numpy, machine learning data science course, machine learning python, data science, python, oak academy, machine learning, python machine learning, python data science, numpy course, data science courseLearn Numpy and get comfortable with Python Numpy in order to start into Data Science and Machine Learning OAK Academy offers highly-rated data science courses that will help you learn how to visualize and respond to new data, as well as develop innovative new technologies Whether you’re interested in machine learning, data mining, or data analysis, Udemy has a course for you Data science is everywhere Better data science practices are allowing corporations to cut unnecessary costs, automate computing, and analyze markets Essentially, data science is the key to getting ahead in a competitive global climate Python Numpy, Python instructors on OAK Academy specialize in everything from software development to data analysis, and are known for their effective, friendly instruction for students of all levels Whether you work in machine learning or finance, or are pursuing a career in web development or data science, Python is one of the most important skills you can learn Python's simple syntax is especially suited for desktop, web, and business applications Python's design philosophy emphasizes readability and usability Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization The core programming language is quite small and the standard library is also large In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasksAre you re
Selamat datang di program Pelatihan Data Science dan Machine Learning Dengan R!Pelatihan ini diperuntukan untuk rekan - rekan ingin belajar data science dan machine learning dari sudut terapan dengan memanfaatkan R.Bagi rekan - rekan yang belum menguasai pemrograman R, pelatihan juga memberikan konten pemrograman dasar untuk Rsehingga rekan - rekan dapat mengikuti pelatihan ini dengan baik. Bagi yang sudah bisa pemrograman R, rekan - rekan dapat melanjutkan di topik berikutnya.Seluruh konten didalam pelatihan ini dilaksanakan secara step - by - step (langkah demi langkah) dan berurutan sehingga ini diharapkan semua peserta dapat dengan mudah mengikuti semua praktikum yang diberikan didalam pelatihan ini. Diharapkan semua peserta dapat mengikuti konten pelatihan ini secara berurutan ;).Berikut ini konten yang akan diberikan pada pelatihan ini.Persiapan pelatihanPemrograman RPengenalan tool dan editor seperti RStudio, Jupyter Notebook / JupyterLab, Jupyter / Notebook Dengan Anaconda, dan Google ColabVisualisasi DataVisualisasi Data dengan ggplot2Dataset, Pra-Proses dan Pengurangan Dimensi FeatureManipulasi dan Analisa dataEksplorasi data science dan machine learningPermasalahan dan Penyelesaian Kasus Linear RegressionPermasalahan dan Penyelesaian Kasus Klasifikasi (Classification)Permasalahan dan Penyelesaian Kasus Kekelompokkan (Clustering)Ensemble MethodsHyperparameter Tuning Untuk Model Machine LearningKumpulan Studi KasusJika ada hal - hal yang ingin ditanyakan mengenai topik diatas, rekan - rekan dapat langsung ditulisnya di ruang diskusi pada web ini sehingga rekan-rekan lainnya dapat mengetahui dan ikut terlibat diskusinya.
Cette formation est conçue pour donner une compréhension complète de la data science, avec un focus particulier sur l’utilisation du langage R, un des outils les plus performants pour l’analyse statistique et la visualisation de données. Voici ce que vous apprendrez au cours de cette formation :Les bases de R et de la data science : Nous commencerons par les bases du langage R, afin que vous puissiez maîtriser les outils essentiels de manipulation et d’analyse de données.Visualisation des données : L’une des compétences les plus recherchées aujourd’hui est la capacité à visualiser des données de manière claire et percutante. Nous apprendrons ensemble à utiliser des bibliothèques comme ggplot2 pour créer des graphiques informatifs.Nettoyage et préparation des données : Une partie essentielle de l’analyse de données consiste à préparer les jeux de données. Vous apprendrez à manipuler, transformer et nettoyer des données brutes pour les rendre exploitables.Algorithmes de machine learning : algorithmes d'apprentissage supervisé et non supervisé en montrant comment créer des modèles prédictifs pour résoudre des problèmes réels.Applications concrètes et projets : Tout au long de la formation, vous aurez l’occasion de travailler sur des exemples et des études de cas, afin de renforcer vos compétences et de vous préparer à intégrer le monde professionnel de la data science.
Fundamentos da linguagem de programação Python , que é a principal base de linguagem para a aplicação da ciência de dadosEstudo das principais funcionalidades da biblioteca Pandas , que é a principal biblioteca de manipulação de dados da Data ScienceEstudo das principais funcionalidades da biblioteca Numpy , que é a principal biblioteca de manipulação de operações matemáticasEstudo das principais bibliotecas de Visualização de Dados : Matplotlib e SeabornManipulando TimeSeries, que são os tipos usados em datas e horasRedução de Dimensões com PCA e TSNEEstatística para Data Science.Machine Learning , com teoria e aplicação prática de estratégias básicas e avançadasIntuição e aplicação dos seguintes modelos preditivos:Linear_Regression (Regressão Linear) Logistic_Regression (Regressão Lógica)Decision_Tree (Árvore de Decisão)Random_Forest (Floresta Aleatória)Stochastic_Gradient_Descent (SGD)Support_Vector_Machine (SVM) AdaBoostGradient_Boost (Impulsionamento Gradiente)K-Means_Clustering - (K-Médias de Grupos)K-Nearest_Neighbors (KNN) PROJETO: Predição da Idade dos Passageiros do Titanic (Regressão Linear)PROJETO: Classificação de sobrevivência dos passageiros do Titanic (Classificação)PROJETO: Análise de Sentimentos de Frases do Twitter (Processamento de Linguagem Natural - PLN)PROJETO: Funcionamento e uso do modelo de detecção e classificação de objetos em imagens e vídeos YOLO (Visão Computacional)PROJETO: Segregando Clientes por Padrões de
This course focuses on the principles of data-driven decision-making. You will learn about data exploration, statistical inference, and how to build and interpret predictive models, including regression.
¡Hola a todos y bienvenidos a este curso sobre los fundamentos del Machine Learning y su aplicación en la solución de problemas reales mediante el uso de Python 3! Mi nombre es Santiago Hernández y voy a ser vuestro instructor a lo largo de este programa formativo, tenéis más información sobre mí en la biografía o en el vídeo “Presentación del instructor”.A lo largo de este curso sobre Machine Learning y Data Science presentaré, desde un nivel muy básico y al alcance de todo tipo de perfiles, los fundamentos teóricos y matemáticos que se necesitan para comprender en detalle el funcionamiento de los algoritmos de aprendizaje automático y las técnicas de ciencia de datos más importantes en la actualidad. Para ello, utilizaré el enfoque que mejores resultados me ha proporcionado al impartir este tipo de clases en diferentes universidades, un enfoque práctico, en el que veréis como se desarrollan las diferentes funciones y ecuaciones matemáticas de mi puño y letra. Representaré gráficamente todas las intuiciones matemáticas en las que se fundamenta el Machine Learning, de manera que, cualquier persona pueda comprenderlas y avanzar con las siguientes secciones. Este no es un curso para matemáticos, es un curso para todos aquellos que quieren adentrarse en el dominio del aprendizaje automático aprendiendo unas bases sólidas que le permitan solucionar problemas reales mediante la implementación en Python 3 de las principales técnicas existentes y comprender aquellos algoritmos que surjan en el futuro.A medida que vayamos construyendo y comprendiendo estos fundamentos teóricos, iremos aplicándolos a casos de uso prácticos en los que utilizaremos conjuntos de datos reales. Yo soy un firme creyente de que aquellas cosas que se aprenden de manera teórica deben saberse aplicar a casos de uso prácticos para sacarles todo el rendimiento posible, y
Datascience; machine learning, data science, python, statistics, statistics, r, machine learning python, deep learning, python programming, djangoHello there,Welcome to “ Kaggle - Get Best Profile in Data Science & Machine Learning ” course.Kaggle is Machine Learning & Data Science community. Boost your CV in Data Science, Machine Learning, Python with KaggleKaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.Machine learning isn’t just useful for predictive texting or smartphone voice recognition. Machine learning is constantly being applied to new industries and new problems. Whether you’re a marketer, video game designer, or programmer, Oak Academy has a course to help you apply machine learning to your work. It’s hard to imagine our lives without machine learning. Predictive texting, email filtering, and virtual personal assistants like Amazon’s Alexa and the iPhone’s Siri, are all technologies that function based on machine learning algorithms and mathematical models.Data science application is an in-demand skill in many industries worldwide — including finance, transportation, education, manufacturing, human resources, and banking. Explore data science courses with Python, statistics, machine learning, and more to grow your knowledge. Get data science training if you’re into research, statistics, and analytics.Kaggle offers a no-setup, customizable, Jupyter Notebooks environment. Access free GPUs and a huge repository of community-published data & code.Kaggle is
This program is designed for business leaders who want to understand the fundamentals of data science and how to apply it in their organizations. It covers key concepts, including machine learning and regression.
Dieser Kurs ist dein umfassender Einstieg in die Welt des Deep Learnings – mit einem klaren Fokus auf Praxis, fundierter Theorie und moderner Python-Entwicklung mit TensorFlow 2 und Keras.Statt nur Code-Schnipsel zu kopieren, lernst du wirklich zu verstehen, wie neuronale Netze funktionieren – von der mathematischen Basis bis zur Anwendung. Du wirst eigene Modelle Schritt für Schritt selbst aufbauen und trainieren, Bilddaten analysieren und sogar Texte mit KI verarbeiten.Du startest mit den Grundlagen des Machine Learning und neuronaler Netzwerke – und steigst dann tief in die wichtigsten Netzarchitekturen ein: Von klassischen Fully Connected Networks über CNNs für Bildverarbeitung bis zu RNNs/LSTMs für Zeitreihen und Texte. Dabei kommen State-of-the-art Modelle wie ResNet und DenseNet ebenfalls nicht zu kurz.Auch Natural Language Processing (NLP) ist Teil des Kurses – perfekt, um moderne KI-Anwendungen wie Chatbots oder Textklassifizierer zu entwickeln.Kursinhalte im Überblick:Einführung in Machine Learning und neuronale NetzeMathematische Grundlagen (z. B. Aktivierungsfunktionen, Backpropagation)Eigene Modelle in TensorFlow 2 und Keras entwickelnVisualisierung und Debugging mit TensorBoardDigitale Bildverarbeitung mit CNNsModerne Architekturen: ResNet, DenseNetSequenzmodelle: RNNs und LSTM für zeitabhängige DatenEinstieg in Natural Language Processing (NLP) mit KerasPraxisnahe Projekte und ÜbungenZiel:Werde fit im Umgang mit modernen KI-Technologien und baue deine eigenen Deep-Learning-Modelle – fundiert, praxisnah, professionell.<p
This course from the University of London provides an in-depth look at the role of data science and AI in human resources. It covers how machine learning algorithms can be applied to workforce analytics, including behavioral and performance assessments. The curriculum explores the ethical implications of using AI in hiring and employee management.
A comprehensive course that teaches machine learning concepts through visual explanations and hands-on exercises. Perfect for beginners wanting to understand ML fundamentals.
Learn deep learning from scratch with intuitive explanations. Build neural networks step by step without relying on complex frameworks.
A practical introduction to ML for developers. Learn to implement ML algorithms and integrate them into software applications.
edX offers a variety of courses and programs on edge computing, covering fundamentals, use cases, and hardware and software components. Learners can gain hands-on experience with edge computing tools and technologies.
Master natural language processing techniques using Python. Learn text processing, sentiment analysis, and language models.
Python course for developers already familiar with other languages. Fast-track your Python learning.
Prepare for Python coding interviews with data structures, algorithms, and problem-solving patterns.
Complete learning path to become a data scientist covering Python, statistics, ML, and practical projects.
Learn to build production-ready applications using large language models. Covers prompting, RAG, and deployment.
Complete learning path covering Python, ML fundamentals, deep learning, and practical ML engineering skills.
Master AI engineering from fundamentals to advanced topics including LLMs, computer vision, and deployment.
Learn skills to operate at Staff+, from safe AI integration to crisis leadership now. This free course maps your path to impact engineering.
Explore the design of scalable generative AI systems guided by a structured framework and real-world systems in text, image, audio, and video generation.
The advanced MCP course teaches you to build agentic apps, integrate LlamaIndex, ensure observability, deploy multi-server systems, and create an Image Research Assistant.
Lead the GenAI revolution with this CrewAI course. Build and manage AI agents capable of intricate workflows, leverage agentic workflows, integrate LLMs, and future-proof your skills in AI automation.
Learn to design clear, structured, and secure prompts that guide AI systems with confidence. Develop skills in context grounding, tool use, evaluation, and designing production-ready prompts.
An introductory course on learning analytics that covers fundamental theories, processes, and different types of educational data. Students will gain experience with educational data sets and the R programming language.
Sharpen your skills for AI interviews by diving deep into neural networks, NLP, and transformer models. Master techniques like gradient descent, transfer learning, and model evaluation to stand out.
Explore Google BERT, fine-tune NLP tasks, discover variants, and build real-world applications with cutting-edge transformer models.
Learn advanced RAG techniques in this advanced RAG course. Explore pre- and post-retrieval optimization with LangChain, and build intelligent, scalable applications with hands-on projects.
Learn to build real-world AI applications in GO language using LangChain and vector databases like PostgreSQL, Pinecone, and Redis.
This course will teach you to design, build, and optimize AI chatbots using transformers and RAG through hands-on projects and Streamlit UI.
Learn advance GitHub Copilot skills with prompt engineering, AI code review, refactoring, and CI/CD/pull request automation. Collaborate, code, and lead development using Copilot.
This course guides you through Llama Stack and its key components, including agentic workflows, RAG systems, safety mechanisms, monitoring, and deployment.
Build, test, and deploy full-stack apps with Windsurf AI, Cascade, and JavaScript. Use AI for coding, testing, reviews, and multimodal productivity.
In this GraphRAG course, you will learn how to use a knowledge graph to implement an RAG application with Neo4j and OpenAI ChatGPT, enhancing response accuracy and reducing hallucinations.
Explore RAG with Google Gemini. Learn its architecture, APIs, and capabilities. Build hands-on applications, integrate LangChain, and create a customer service assistant with multimodal AI prompts.
Learn how to master responsible AI. Learn fairness, bias mitigation, explainable AI, and data privacy to design ethical AI systems. Future-proof your skills in trustworthy AI practices.
This course teaches you about DeepSeek, the latest upgrade in DeepSeek's reasoning model, and how it competes with OpenAI's upcoming o3.
This edX course focuses on the fundamentals of supervised machine learning, including both classification and regression. You will learn to apply various algorithms to real-life problems using Python and scikit-learn. The curriculum covers classification techniques and important concepts for evaluating and tuning your models.
Part of the Data Science Professional Certificate, this course covers popular machine learning algorithms, principal component analysis, and regularization. You will build a movie recommendation system.
This course helps you learn essential foundational math concepts for AI and machine learning, like calculus, linear algebra, and statistics, using a hands-on approach with Python.
This course from Microsoft introduces the fundamental principles of machine learning using Python. You will learn about various machine learning algorithms, including regression, and how to implement them.
This course from Columbia University introduces the fundamental concepts of statistical thinking for data science. It covers topics such as probability, sampling, estimation, and hypothesis testing. The course emphasizes the practical application of these concepts to real-world data problems.
This course, part of the TinyML Professional Certificate series, delves into the practical applications of Tiny Machine Learning. Students explore the code behind widely used TinyML applications like keyword spotting, visual wake words, and anomaly detection. The course uses real-world industry applications to illustrate the principles of TinyML.
As a key component of the TinyML Professional Certificate, this course offers hands-on experience in deploying machine learning models on small embedded devices. Students learn to program in TensorFlow Lite for Microcontrollers, write the necessary code, and deploy their models to a tiny microcontroller. The course utilizes a TinyML Program Kit that includes an Arduino board for practical projects.
This course from Harvard University introduces the field of Tiny Machine Learning (TinyML), which involves running machine learning models on low-power microcontrollers. It covers the fundamentals of deep learning, data collection, and model deployment on embedded devices, with a focus on applications like keyword spotting and image classification.
A MicroMasters program that includes a course on machine learning fundamentals, covering tree-based models and ensemble methods.
A self-paced course that provides a solid knowledge base in statistics, linear algebra, multivariable calculus, and probability for AI.
This course introduces the fundamentals of reinforcement learning, guiding learners on how to frame RL problems and tackle classic examples. It covers basic algorithms and progresses to using function approximation with deep learning. It also features 'Project Malmo' for AI experimentation within Minecraft.
This program teaches how to use Python for data analysis in a business context, including data wrangling and visualization for EDA.
This course explores how learning analytics, machine learning, and AI integrate into modern L&D systems, featuring real LMS case studies. It is ideal for technical L&D professionals and data-informed instructional designers.
A search result page on edX for courses related to optimization for machine learning, featuring courses from various universities.
This article and related courses on DataCamp discuss how AI is used in retail to enhance operational efficiency and personalize customer experiences, including AI-driven visual merchandising and predictive analytics.
This course focuses on A/B testing, a common application of hypothesis testing in the industry. You will learn how to design and analyze A/B tests using Python. The course covers topics such as sample size calculation, statistical power, and the interpretation of results.
Learn the role Generative Artificial Intelligence plays today and will play in the future in a business environment. This course covers how a well-designed AI initiative connects business goals, data readiness, people, and technology into one framework.
This case study focuses on analyzing churn rates for a telecom company using Tableau. It covers creating calculated fields, various visualizations, and combining them into a story to share insights.
This course provides a practical guide to data cleaning in R, covering everything from common data problems to techniques for tidying data.
This course focuses on unsupervised learning, specifically clustering algorithms. It covers popular methods like K-Means and hierarchical clustering, and teaches how to apply them to real-world datasets.
This course covers the process of exploring and analyzing data, from understanding a dataset to incorporating findings into a data science workflow. You will use Python to summarize, validate, and clean data.
This course teaches how to use graphical and numerical techniques in R to uncover the structure of your data and identify interesting relationships and unusual observations.
A project-based course where you'll apply EDA techniques to a real-world dataset of UN voting records, using R packages like dplyr and ggplot2.
A hands-on course that covers various aspects of feature engineering for both categorical and continuous variables, as well as text data.
Learn to extract useful information from text and format it for machine learning models. The course covers POS tagging, named entity recognition, readability scores, and implementing tf-idf models using scikit-learn and spaCy.
Learn various feature engineering techniques in R to develop meaningful features. The course covers changing categorical features to numerical, manipulating numeric features, and transformation techniques like Box-Cox.
This course focuses on data wrangling and feature engineering with large datasets using PySpark. It covers preparing and cleaning data, creating new features, and building and evaluating a machine learning model.
Learn how to tune your model's hyperparameters to get the best predictive results for your supervised learning models in R.
This hands-on course from DataCamp teaches you how to conduct hypothesis tests in Python. You will learn about different types of tests, including t-tests and chi-squared tests, and how to interpret their results. The course is interactive and includes many coding exercises.
This course introduces you to regression analysis using the statsmodels library in Python. You'll learn how to build, interpret, and evaluate linear regression models.
Learn the essentials of the Tidyverse in R for data wrangling and visualization, which includes powerful tools for cleaning and transforming data.
Learn to build, interpret, and tune linear classifiers, including logistic regression and support vector machines, using scikit-learn.
This course teaches you how to use tree-based models and ensembles for classification and regression in R.
Learn the fundamentals of optimization and how to apply them to data science problems using Python.
This course focuses on the pandas library, a powerful tool for data manipulation and analysis in Python. It is a great precursor to learning about regression and other machine learning techniques.
This course focuses on regularization techniques, such as Ridge and Lasso regression, which are used to prevent overfitting in machine learning models. You will learn the theory behind these techniques and how to apply them in practice.
This course teaches the fundamentals of statistical thinking using Python. You will learn to perform exploratory data analysis, think probabilistically, and understand the core concepts of statistical inference.
Learn to generate, explore, and evaluate machine learning models in R using the Tidyverse. The course covers multiple and logistic regression, tree-based models, and support vector machines.
This course covers four of the most common classification algorithms in R: k-nearest neighbors, logistic regression, Naive Bayes, and decision trees.
This course teaches you how to build predictive models using scikit-learn. You'll learn about classification and regression and apply your skills to real-world datasets.
This tutorial explains the process of gathering, collecting, and transforming raw data into another format for better understanding and analysis using the Pandas framework in Python. It covers data exploration, handling missing values, reshaping data, and filtering.
This course focuses on the critical skill of visualizing time series data to identify patterns, trends, and seasonality using Python libraries like Matplotlib and Seaborn.
A DataCamp project that dives into India's telecom sector to analyze customer churn. You'll use pandas and machine learning to study datasets from top telecom firms.
A 26-hour interactive course focused on building AI applications like chatbots and semantic search engines using LLMs and vector databases. It covers tools like the OpenAI API, Hugging Face, Langchain, and Pinecone for vector embeddings.
A career track focused on using R for data analysis, covering data manipulation, visualization, and case studies to build practical EDA skills.
Unlock the power of data and empower yourself to tackle complex data challenges. There's no prior knowledge or coding skills required. You'll become more data literate through hands-on exercises and explore reading, working with, analyzing, and communicating with data.
A career track on DataCamp that provides a comprehensive curriculum for aspiring machine learning scientists. It covers a wide range of topics, including supervised and unsupervised learning, deep learning, and natural language processing, with a focus on practical coding exercises.
A comprehensive skill track that covers various aspects of time series analysis in Python, from manipulation and visualization to statistical modeling and machine learning.
A tutorial that covers the essentials of synthetic data generation, including various techniques and tools. It provides practical Python code examples for creating synthetic data for AI and machine learning.
This training focuses on managing features for machine learning models to save time and improve consistency. It teaches best practices for feature engineering and how to reuse features across projects using a feature store.
This course provides guidance on balancing AI innovation with risk mitigation, covering frameworks for AI governance, Safety by Design strategies, risk assessment methods, red teaming approaches, and regulatory compliance essentials. It is designed for professionals in Trust & Safety, AI engineering, policy, and brand management.
Explore how to make powerful, accurate predictions with ensemble learners, one of the most common classes of machine learning algorithms.
This course shows you how to apply supervised learning techniques to real-world problems, focusing on both classification and regression tasks. You'll start with basic models and advance to more complex algorithms like decision trees and XGBoost.
Designed for business leaders, managers, and technical professionals, this online program provides an understanding of the mechanics behind AI, its future potential, and possible challenges, covering topics from the history of AI to driving AI in business.
This course provides a foundational understanding of linear regression, one of the most important algorithms in machine learning and AI. It covers the theory and practical implementation of linear regression.
Learn about the modeling techniques and experimental designs that allow you to establish causal inference, and gain insights into causal inference from observational studies.
A learning path that provides a comprehensive overview of how people analytics and AI are transforming human resources. The courses cover topics such as using data and AI to make better hiring decisions, improve employee performance, and reduce turnover. It touches upon how AI can be used for talent assessment and understanding workforce dynamics.
This course provides a step-by-step guide to using Python for data analysis, including data cleaning, manipulation, and visualization for EDA.
This intermediate-level course explains how to create one of the most common types of machine learning: supervised learning models.
A collection of courses on LinkedIn Learning focused on optimization techniques for machine learning and data science.
A specialized course that explores how artificial intelligence can enhance UX design processes. It covers practical tools, ethical considerations, and strategies to create inclusive and effective AI-driven designs.
This course explores how AI tools can enhance the UX design workflow, covering ideation, prototyping, and user testing, with a focus on balancing AI assistance with human creativity.
Learn how to detect and respond to anomalies using Metricbeat and the Elastic Stack for enhanced enterprise monitoring.
This course on Pluralsight teaches how to train custom machine learning models on your own datasets using Google Cloud AutoML. It covers the underlying concepts of neural architecture search and transfer learning used by AutoML.
This course introduces learners to Vertex AI as a unified platform for building, training, and deploying AutoML machine learning models. It discusses the five phases of converting a use case to be driven by machine learning, emphasizing the importance of each step.
A course on Pluralsight that covers the end-to-end process of time series forecasting in Python, from data exploration to model deployment.
A learning path on Pluralsight that teaches how to apply common feature engineering techniques as part of the machine learning workflow specifically within the Microsoft Azure platform.
This course introduces the tools and techniques used in applied data science, including methods for data cleaning and preparation.
Introduces the role of AI in education and workplace learning, covering ethics, automation, and the future of teaching. Ideal for L&D managers, CLOs, and instructional leaders.
Learn how mathematics, particularly numerical linear algebra, underpins big data analysis.
This course covers key mathematical concepts for machine learning and AI, with a focus on implementation using R.
This course delves into the societal impact of facial recognition technology, which is increasingly a part of our daily lives. It covers the technical and design issues that can lead to biased performance across different societal groups and emphasizes the need for responsible deployment to mitigate these inherent biases.
In partnership with AMD, this course teaches how to apply fine-tuning and reinforcement learning to improve LLM behavior, reasoning, and safety. You will learn about the post-training lifecycle, core techniques like RLHF and LoRA, and how to design evaluations to detect issues like reward hacking and diagnose failures.
This professional certificate teaches how to build and train deep learning models using PyTorch. It covers applying transfer learning and fine-tuning to pretrained models for computer vision and natural language processing.
This guide explains how to integrate machine learning capabilities directly into iOS applications using Apple's Core ML framework for on-device inference.
A specialization that teaches how to deploy machine learning models on devices, train and run models in browsers and mobile applications, and retrain deployed models while protecting privacy.
Taught by instructors from LiveKit and an Andreessen Horowitz portfolio company, this course covers how to build scalable voice agents using a cloud infrastructure. It delves into the components of a voice pipeline and real-time networking protocols.
This course teaches how to build multimodal search and RAG systems. It covers implementing contrastive learning for modality-independent embeddings, building multimodal RAG systems that reason over multimodal context, and implementing industry applications like multi-vector recommender systems.
This intermediate course teaches best practices for using Claude Code to improve your coding workflow. You will learn to explore, develop, test, refactor, and debug codebases with this highly agentic AI assistant.
This course introduces Machine Learning Operations tools to manage the complexities of AI projects. You will learn to use Weights & Biases to track experiments, version data, and collaborate. The course covers instrumenting a Jupyter notebook, managing hyperparameters, logging metrics, and tracing prompts and responses to LLMs over time.
This course, offered by DeepLearning.AI and taught by the founder and CEO of crewAI, focuses on building multi-agent systems to automate complex business processes using the open-source crewAI library.
This course, developed in collaboration with Hugging Face, teaches the fundamentals of model quantization. You will learn to compress large models, making them more accessible and efficient, using the Hugging Face Transformers library and Quanto.
Learn how to make safer LLM apps by attacking various chatbot applications using prompt injections to understand security failures. This course, in collaboration with Giskard, teaches industry-proven red teaming techniques to proactively test, attack, and improve the robustness of your LLM applications.
Taught by the co-founder & CEO of GuardrailsAI, this course teaches you to use guardrails to prevent common LLM issues like hallucinations and sensitive information leaks. You'll add guardrails to a RAG-powered chatbot and learn to build custom protections.
Discover how AI can enhance productivity, improve cognition, and accelerate discovery in public sector organizations and learn about the importance of responsible AI practices.
This module teaches you how to apply slash commands, interact with GitHub Copilot using the Chat feature, and ask questions about your project using an agent to make changes and updates to a Python application.
A learning path that introduces educators to AI concepts and tools from Microsoft. It includes a module on AI tools for educators and accessibility, demonstrating how AI can support personalized and inclusive learning.
A free, self-paced learning path designed to help educators explore the potential of artificial intelligence in education. It covers essential AI concepts, techniques, tools, and practical applications to enhance teaching and learning experiences.
An interactive course that teaches the fundamentals of EDA in Python, covering summary statistics, data visualization, and preparing data for machine learning models.
Learn how to use GitHub Copilot to streamline your workflow and development. This course provides an in-depth understanding of the AI-driven coding assistant.
A free and in-depth course on multivariable calculus, an essential topic for understanding optimization in machine learning.
An introductory course that covers the principles of data analysis and data visualization. You'll learn how to use statistical analysis to guide business decisions.
A free, self-paced course covering the concepts of anomaly and outlier detection, including handling missing values and data visualization.
Learn Deep Learning A-Z: Hands-On Neural Networks
Learn Computer Vision A-Z: Learn OpenCV, GANs and Deep Learning
A hybrid program for energy engineers to apply data science and AI techniques. It combines theoretical knowledge with practical use cases, focusing on analyzing, forecasting, and optimizing energy use with Python.
A comprehensive and free resource for learning foundational math topics at your own pace. Khan Academy offers extensive video libraries and practice exercises for linear algebra, differential and integral calculus, and probability and statistics.
This course is designed for compliance professionals to understand and implement AI solutions. It covers AI fundamentals, practical applications in regulatory contexts, and includes case studies on successful AI implementation in compliance.
This masterclass is designed for cybersecurity professionals, AI Trust & Safety leads, and product managers. It focuses on AI security fundamentals, practical techniques with hands-on experience, defensive strategies, and industry best practices.
Learn Data Science and Machine Learning with R
This course focuses on cleaning, normalizing, and creating features to improve the performance of machine learning models.
This course provides a foundational understanding of what data analytics is and the role of a data analyst. It covers topics like thinking like an analyst and gathering useful data.
Comprehensive deep learning specialization by Andrew Ng. Master neural networks, CNNs, RNNs, and modern deep learning architectures.
This course focuses on supervised learning specifically with neural networks, covering deep neural networks, convolutional networks, and sequence classifiers.
A course on LinkedIn Learning covering the fundamentals of time series analysis using Python, including data preparation, modeling, and forecasting.
This course explores how to build custom real-time voice-to-voice AI chat into front-end web apps using OpenAI's Realtime API and WebRTC. It covers setting up secure WebRTC connections and provides hands-on examples with JavaScript.
An introductory course to computer vision that covers image processing and the practical application of the OpenCV library with Python for AI and Machine Learning tasks. It provides insights into various methods for working with images.
A hands-on course on building an AI-enhanced recipe application using FlutterFlow. It covers user authentication, database management, and integrating OpenAI's API for smart recipe suggestions.
Offers hands-on training in deep learning and accelerated computing for healthcare, with courses available as self-paced online or instructor-led workshops.
NVIDIA offers a range of self-paced courses and instructor-led workshops on deep learning, accelerated computing, and data science. These courses provide hands-on experience with GPU-accelerated servers in the cloud and offer certificates upon completion.
This course covers various optimization techniques and their applications in machine learning.
A 5-day intensive course focusing on mastering AI's role in contract drafting, negotiation, risk management, and dispute resolution. It covers practical applications like automating contract reviews and managing contractual risks using AI tools.
This course teaches how to apply AI to detect suspicious behavior and anomalies that signal potential security threats.
Learn different techniques to build a model for anomaly detection specifically for time series datasets.
This introductory course explores the role of AI in transforming farming practices, including precision agriculture and sustainable crop management. Key topics include AI for precision agriculture, crop and soil management, and agricultural robotics and automation. No prior experience is required.
This course teaches you how to use image classifiers to perform object detection, recognition, and tracking using Tensorflow. By the end of this course, you'll have the skills and knowledge needed to create an image classifier.
This course introduces Elasticsearch, focusing on the basic building blocks of search algorithms and the underlying data structures. It covers installation, indexing, performing various types of search queries, and exploring the TF/IDF algorithm for search ranking and relevance.
This course teaches you how to use R to apply clustering, dimensionality reduction, and anomaly detection techniques to explore and analyze unlabeled datasets, including algorithms like k-means, hierarchical clustering, and DBSCAN.
This learning path introduces the practical use of AI and machine learning in cybersecurity, covering how AI enhances threat detection, anomaly detection, and automated incident response.
An AI-powered coaching platform that provides daily micro-coaching sessions to help with personal growth, goal tracking, and building self-improvement habits.
This course covers various feature engineering techniques to get the best results from a machine learning model, including feature selection (filter, wrapper, and embedded methods) and feature extraction from image and text data.
This training course teaches you to create a winning product strategy and an outcome-based roadmap in the age of AI. It emphasizes leveraging AI to identify strategic opportunities and speed up strategy work.
This course is designed for Agile professionals with at least 3 years of experience. It focuses on using AI to optimize Agile processes, enhance sprint planning, and automate retrospectives with tools like ChatGPT, Jira, and Trello.
An introductory course that covers the fundamentals of managing and deploying Large Language Models (LLMs) in production. It includes topics on deployment, monitoring, fine-tuning, scaling, and automating workflows using tools like TensorFlow and Hugging Face.
This free course covers the fundamentals of supervised learning, including regression, classification, and clustering.
An 8-week online course that teaches the tools teams use to collect player data, parse with SQL, unify with Databricks, apply data science with R and Python, evaluate results, and visualize.
Träumst du davon...die Möglichkeiten von KI-Tools wie ChatGPT, Claude, Gemini und Neuroflash voll auszuschöpfen, um produktiver und kreativer zu arbeiten?mit präzisen Prompts die besten Ergebnisse aus generativer KI herauszuholen und deinen Workflow zu optimieren?Prompts zu erstellen, die für verschiedene Anwendungen wie Content-Erstellung, Produktivität, Marketing oder Automatisierung maßgeschneidert sind?deine Fähigkeiten im Umgang mit Large Language Models (LLM) zu erweitern, um innovative Lösungen für persönliche und berufliche Herausforderungen zu entwickeln?Dann ist dieser Kurs „Prompt Engineering für KI: ChatGPT, Claude, Gemini und LLM“ genau das Richtige für dich! Von der Einführung in die grundlegenden Funktionen bis hin zu fortgeschrittenen Strategien zeigt dir dieser Kurs, wie du die volle Leistung moderner KI-Tools nutzen kannst. Lerne, wie du mit gezielten Prompts Ergebnisse erzielst, deine Arbeitsprozesse vereinfachst und sogar neue kreative Ansätze entwickelst. Entdecke, wie generative KI nicht nur deine Projekte voranbringt, sondern auch eine neue Dimension von Effizienz und Innovation eröffnet.Was du lernen wirst:Einführung in ChatGPT, Gemini und Claude: Du erhältst eine Einführung in die führenden KI-Tools ChatGPT, Google Gemini und Claude. Der Kurs zeigt dir, wie du einen kostenlosen Account erstellst, dich mit den Benutzeroberflächen vertraut machst und erste Prompts eingibst. Außerdem erfährst du, wie du mit jedem Tool gezielt arbeiten kannst, unabhängig von deiner bisherigen Erfahrung.Erste Prompts bei LLM's eingeben: Lerne, wie du effektive Prompts formulierst, die dir präzise und hilfreiche Ergebnisse liefern. Du erfährst, wie du B
Extremely Hands-On... Incredibly Practical... Unbelievably Real!This is not one of those fluffy classes where everything works out just the way it should and your training is smooth sailing. This course throws you into the deep end.In this course you WILL experience firsthand all of the PAIN a Data Scientist goes through on a daily basis. Corrupt data, anomalies, irregularities - you name it!This course will give you a full overview of the Data Science journey. Upon completing this course you will know:How to clean and prepare your data for analysisHow to perform basic visualisation of your dataHow to model your dataHow to curve-fit your dataAnd finally, how to present your findings and wow the audienceThis course will give you so much practical exercises that real world will seem like a piece of cake when you graduate this class. This course has homework exercises that are so thought provoking and challenging that you will want to cry... But you won't give up! You will crush it. In this course you will develop a good understanding of the following tools:SQLSSISTableauGretlThis course has pre-planned pathways. Using these pathways you can navigate the course and combine sections into YOUR OWN journey that will get you the skills that YOU need.Or you can do the whole course and set yourself up for an incredible career in Data Science.The choice is yours. Join the class and start learning today!See you inside,Sincerely,Kirill Eremenko
As a practitioner of Deep Learning, I am trying to bring many relevant topics under one umbrella in the following topics. Deep Learning has been most talked about for the last few years and the knowledge has been spread across multiple places.1. The content (80% hands-on and 20% theory) will prepare you to work independently on Deep Learning projects2. Foundation of Deep Learning TensorFlow 2.x3. Use TensorFlow 2.x for Regression (2 models)4. Use TensorFlow 2.x for Classifications (2 models)5. Use Convolutional Neural Net (CNN) for Image Classifications (5 models)6. CNN with Image Data Generator7. Use Recurrent Neural Networks (RNN) for Sequence data (3 models)8. Transfer learning9. Generative Adversarial Networks (GANs)10. Hyperparameters Tuning11. How to avoid Overfitting12. Best practices for Deep Learning and Award-winning Architectures
In This Course, Solve Business Problems Using Data Science Practically. Learn To Build & Deploy Machine Learning, Data Science, Artificial Intelligence, Auto Ml, Deep Learning, Natural Language Processing (Nlp) Web Applications Projects With Python (Flask, Django, Heroku, AWS, Azure, GCP, IBM Watson, Streamlit Cloud).Data science can be defined as a blend of mathematics, business acumen, tools, algorithms, and machine learning techniques, all of which help us in finding out the hidden insights or patterns from raw data which can be of major use in the formation of big business decisions.In data science, one deals with both structured and unstructured data. The algorithms also involve predictive analytics. Thus, data science is all about the present and future. That is, finding out the trends based on historical data which can be useful for present decisions, and finding patterns that can be modeled and can be used for predictions to see what things may look like in the future.Data Science is an amalgamation of Statistics, Tools, and Business knowledge. So, it becomes imperative for a Data Scientist to have good knowledge and understanding of these.With the amount of data that is being generated and the evolution in the field of Analytics, Data Science has turned out to be a necessity for companies. To make the most out of their data, companies from all domains, be it Finance, Marketing, Retail, IT or Bank. All are looking for Data Scientists. This has led to a huge demand for Data Scientists all over the globe. With the kind of salary that a company has to offer and IBM is declaring it as the trending job of the 21st century, it is a lucrative job for many. This field is such that anyone from any background can make a career as a Data Scientist.In This Course, We Are Going To Work On 50 Real World Projects Listed Below:Project-1: Pan Card Tempering Detector App -Deploy On Heroku
This course provides practical advice on how to learn more effectively. It covers topics such as chunking, memory techniques, and procrastination. While not exclusively about AI, it is highly relevant to the 'learning to learn' aspect and is one of the most popular courses on Coursera.
Course DetailsPrompt Engineering is the skill that will be required to stay on the job in the future because automation using AI tools is increasing day by day and it is creating new job roles called Prompt Engineers.You know that automation replaces workers and this will be the future. But some intervention is always needed so Prompt Engineers will be the first persons who will be preferred over others and this is about the prompting skills not only the job skills or knowledge. Of course, skills and knowledge are an important part but you would need to have prompting skills along with knowledge and job skills.Being a content creator, I have spent long hours testing and creating new prompts that I believe are super useful for everyone. So, if you want to learn Prompt Engineering then this course is for you.In this course, you will find all the important prompting techniques and the answers to common questions you might have in your mind about prompt engineering. Also, if you don't find the answer then you can message me anytime.Also, you will get these things in the course:1. List of 70,000+ prompts that you can easily copy and paste2. Structure to create a Perfect Prompt3. A list of 38 AI Tools4. Access to questions and answers forum, where you can post your prompt engineering questions or anytime5. You can also ask questions about ChatGPT not only about Prompt Engineering6. You will get a project file that is very handy to recall all the prompting techniques discussed in the course7. You will be notified first when any new content is added to the courseA brief overview of the Prompts List:64 Prompts to do Copywriting 55 Prompts to do Email Copywriting</
Prompting Playbook & Frameworks — Write Prompts That Deliver Business-Ready Results (ChatGPT, Gemini, Copilot, Perplexity)Short, practical, web-based.Learn the exact frameworks that turn vague asks into clear, reliable outputs across ChatGPT, Google Gemini, Microsoft Copilot (Web), and Perplexity.No agents. No coding. Just prompts that work.Why this course now?AI is already part of everyday work.Meeting notes, status updates, proposals, personas, job descriptions, variance commentary—tasks that used to take hours can now take minutes.But there’s a catch.A basic prompt will give you random, inconsistent responses.A well-structured, well-scoped prompt produces accurate, predictable, decision-ready results.This course gives you the playbook for writing those prompts—fast.What you’ll learnHow to structure any request with R-C-I-E-S so the model understands exactly what to do.How to iterate once or twice with the Draft → Critique → Revise loop and a simple rubric to lift quality without wasting time.How to shape prompts with TCR (Task–Context–Rules), plus variables and checklists that you can reuse anywhere.How to decompose work into a Chain of Tasks when a single prompt is too vague—no agents required.How to ask for bullet caps, word caps, evidence lines, and tables so your outputs are tidy, scannable, and executive-ready.How to apply verification habits (no new numbers, assumptions to verify, objections to test) so stakeholders trust the output.No coding. No installs. Web LLMs only.<p
Have you been looking for a course that teaches you effective machine learning in scikit-learn and TensorFlow 2.0? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques?If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data.The theory will be underpinned with plenty of practical examples, and code example walk-throughs in Jupyter notebooks. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task.By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand (for example, creating an algorithm to read a labeled dataset of handwritten digits).About the AuthorSamuel Holt has several years' experience implementing, creating, and putting into production machine learning models for large blue-chip companies and small startups (as well as within his own companies) as a machine learning consultant.He has machine learning lab experience and holds an MEng in Machine Learning and Software Engineering from Oxford University, where he won four awards for academic excellence.Specifically, he has built systems that run in production using a combination of scikit-learn and TensorFlow involving automated customer support, implementing document OCR, detecting vehicles in the case of s
An IBM-led course that covers a variety of machine learning algorithms, including a section on decision trees and ensemble methods with hands-on labs.
Questo corso sul Data Science con Python nasce per essere un percorso completo su come si è evoluta l'analisi dati negli ultimi anni a partire dall'algebra e dalla statistica classiche. L'obiettivo è accompagnare uno studente che ha qualche base di Python in un percorso attraverso le varie anime del Data Science. Cominceremo con un ripasso delle basi di Python, a partire dallo scaricamento e installazione, all'impostazione dell'ambiente di lavoro, passando per le strutture, la creazione di funzioni, l'uso degli operatori e di alcune funzioni importanti. Passeremo poi a vedere come manipolare e gestire un dataset, estrarne dei casi oppure delle variabili, generare dei dataset casuali, calcolare delle misure statistiche di base, creare grafici con i pacchetti Matplotlib e Seaborn.Nelle sezioni successive cominciamo a entrare nel cuore del Data Science con Python, a cominciare dal preprocessing: vediamo infatti come ripulire e normalizzare un dataset, e come gestire i dati mancanti. La sezione successiva ci permette di cominciare a impostare dei modelli di machine learning con Python: vedremo tutti gli algoritmi più comuni, sia supervisionati che non supervisionati, come la regressione, semplice, multipla e logistica, il k-nearest neighbors, il Support Vector Machines, il Naive Bayes, gli alberi di decisione e il clustering. Passeremo poi ai più comuni metodi ensemble, come il Random Forest, il Bagging e il Boosting, e all'analisi del linguaggio naturale e al suo utilizzo nel machine learning per la catalogazione dei testi.
Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again.In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR.We cover several key NLP frameworks including:HuggingFace's TransformersTensorFlow 2PyTorchspaCyNLTKFlairAnd learn how to apply transformers to some of the most popular NLP use-cases:Language classification/sentiment analysisNamed entity recognition (NER)Question and AnsweringSimilarity/comparative learningThroughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.All of this is supported by several other sections that encourage us to learn how to better design, implement, and measure the performance of our models, such as:History of NLP and where transformers come fromCommon preprocessing techniques for NLPThe theory behind transformersHow to fine-tune transformersWe cover all this and more, I look forward to seeing you in the course!
Common scenario: You try to get into machine learning and data science, but there's SO MUCH MATH.Either you never studied this math, or you studied it so long ago you've forgotten it all.What do you do?Well my friends, that is why I created this course.Linear Algebra is one of the most important math prerequisites for machine learning. It's required to understand probability and statistics, which form the foundation of data science.The "data" in data science is represented using matrices and vectors, which are the central objects of study in this course.If you want to do machine learning beyond just copying library code from blogs and tutorials, you must know linear algebra.In a normal STEM college program, linear algebra is split into multiple semester-long courses.Luckily, I've refined these teachings into just the essentials, so that you can learn everything you need to know on the scale of hours instead of semesters.This course will cover systems of linear equations, matrix operations (dot product, inverse, transpose, determinant, trace), low-rank approximations, positive-definiteness and negative-definiteness, and eigenvalues and eigenvectors. It will even include machine learning-focused material you wouldn't normally see in a regular college course, such as how these concepts apply to GPT-4, and fine-tuning modern neural networks like diffusion models (for generative AI art) and LLMs (Large Language Models) using LoRA. We will even demonstrate many of the concepts in this course using the Python programming language (don't worry, you don't need to know Python for this course). In other words, instead of the dry old college version of linear algebra, this course takes just the most practical and impactful topics, and provides you with skills directly applicable to machine learning and data science, so you can s
Hello!Welcome, and thanks for choosing How to Start & Grow Your Career in Machine Learning/Data Science!With companies in almost every industry finding ways to adopt machine learning, the demand for machine learning engineers and developers is higher than ever. Now is the best time to start considering a career in machine learning, and this course is here to guide you.This course is designed to provide you with resources and tips for getting that job and growing the career you desire.We provide tips from personal interview experiences and advice on how to pass different types of interviews with some of the hottest tech companies, such as Google, Qualcomm, Facebook, Etsy, Tesla, Apple, Samsung, Intel, and more.We hope you will come away from this course with the knowledge and confidence to navigate the job hunt, interviews, and industry jobs.***NOTE This course reflects the instructor's personal experiences with US-based companies. However, she has also worked overseas, and if there is a high interest in international opportunities, we will consider adding additional FREE updates to this course about international experiences.We will cover the following topics:Examples of Machine Learning positionsRelevant skills to have and courses to takeHow to gain the experience you needHow to apply for jobsHow to navigate the interview processHow to approach internships and full-time positionsHelpful resourcesPersonal adviceWhy Learn From Class Creatives?Janice Pan is a full-time Senior Engineer in Artificial Intelligence at Shield AI. She has published papers in the fields of computer vision and video processing and has interned at some
Machine Learning Real value comes from actually deploying a machine learning solution into production and the necessary monitoring and optimization work that comes after it.Most of the problems nowadays as I have made a machine-learning model but what next.How it is available to the end-user, the answer is through API, but how it works?How you can understand where the Docker stands and how to monitor the build we created.This course has been designed to keep these areas under consideration. The combination of industry-standard build pipeline with some of the most common and important tools.This course has been designed into Following sections:1) Configure and a quick walkthrough of each of the tools and technologies we used in this course.2) Building our NLP Machine Learning model and tune the hyperparameters.3) Creating flask API and running the WebAPI in our Browser.4) Creating the Docker file, build our image and running our ML Model in Docker container.5) Configure GitLab and push your code in GitLab.6) Configure Jenkins and write Jenkins's file and run end-to-end Integration.This course is perfect for you to have a taste of industry-standard Data Science and deploying in the local server. Hope you enjoy the course as I enjoyed making it.
Willkommen zum ChatGPT-Kurs, der Ihnen alles beibringt, was Sie über die effektive Nutzung von ChatGPT wissen müssen! Dieser Kurs wurde entwickelt, um Ihnen die notwendigen Fähigkeiten zu vermitteln, um ChatGPT optimal zu nutzen, sei es für persönliche Projekte, berufliche Anwendungen oder kreative Ideen.Kursübersicht:Einführung in ChatGPTVerständnis der Grundlagen: Wie funktioniert ChatGPT?Überblick über die Anwendungsbereiche von ChatGPT in verschiedenen Branchen.Praktische AnwendungsfälleSchreiben von Texten: Tipps und Tricks zur Verbesserung der Textqualität.Kreative Anwendungen: Generierung von Ideen, Geschichten und mehr.Anpassung und FeinabstimmungPersonalisierung von ChatGPT für Ihre speziellen Bedürfnisse.Feinabstimmung von Modellen für branchenspezifische Anforderungen.Integration von ChatGPT in ProjekteEinbindung von ChatGPT in Ihre Arbeitswelt.Best Practices für die nahtlose Anwendung.Effiziente Kommunikation mit ChatGPTRichtiges Formulieren von Anfragen: Maximierung der Antwortqualität.Umgang mit Einschränkungen und Herausforderungen.Ethik und Verantwortung in der Nutzung von ChatGPTSensibilisierung für mögliche Bias-Probleme.Verantwortungsbewusster Einsatz von KI-Technologien.Aktuelle Entwicklungen und ZukunftsaussichtenEinblick in die neuesten Updates und Funktionen von ChatGPT.Ausblick auf zukünftige Entwicklungen in der Welt der KI-gesteuerten Kommunikation.Zielgruppe:EntwicklerContent-Ersteller
Bist du daran interessiert, deine Fähigkeiten im Bereich des Prompt Engineerings zu entwickeln? Möchtest du lernen, wie man perfekte Prompts für KI-Tools wie ChatGPT, Midjourney, Leonardo AI und Stable Diffusion erstellt? Dann ist dieser Kurs genau das Richtige für dich!Als Prompt Engineer spielst du eine entscheidende Rolle bei der Optimierung der Leistungsfähigkeit dieser leistungsstarken KI-Tools. In diesem umfassenden Kurs werden wir gemeinsam daran arbeiten, deine Fähigkeiten im Schreiben von Prompts zu perfektionieren.Der Kurs behandelt alle Details zum Prompt Engineering in ChatGPT, Midjourney und Stable Diffusion. Du erhältst eine umfassende Einführung in diese Tools und erfährst, wie sie funktionieren und sich voneinander unterscheiden. Dabei lernst du die verschiedenen Aspekte des Prompt Engineerings kennen und wie du sie auf diese KI-Tools anwendest.Wir werden die Kunst des effektiven Promptings erforschen und lernen, wie man Inputs für optimale Ergebnisse modifiziert. Du wirst lernen, wie man Prompts für spezifische Anwendungsbereiche erstellt, sei es für Content-Erstellung, Social Media, Werbetexte, SEO oder andere Zwecke.Zusätzlich wirst du Einblicke in die Anwendung von Midjourney und Stable Diffusion gewinnen, um generative Kunstwerke zu erstellen. Du lernst die technischen Hintergründe von LLM und Diffusionsmodellen kennen, um das perfekte Prompt Engineering für Midjourney, Stable Diffusion (mit negativen Prompts) und ChatGPT zu beherrschen.Es ist wichtig zu wissen, dass der Prompt Engineer ein eigenes Berufsfeld ist, das von Unternehmen zunehmend geschätzt wird. Tatsächlich sind Unternehmen oft bereit, hohe Summen für gut ausgebildete Prompt Engineers zu zahlen, da sie die Leistungsfähigkeit ihrer KI-Systeme verbessern wollen, wir werden uns ansehen, was du als Prompt Engineer in einer solchen Rolle wikrlich können musst, wenn du das Hauptberuflich machen möchtest.Mit lebenslangem Zugriff auf den Lerninhalt un
This course focuses on using attention models in Natural Language Processing. You will learn how to build models that can focus on specific parts of an input sequence to improve performance on tasks like machine translation and text summarization.
KI-Agenten sind in aller Munde, doch kaum jemand weiß, was sie sind und noch weniger, wie man sie verwendet. Tools wie CrewAI, Autogen, BabyAGI, LangChain, LangGraph usw. klingen komplexer, als sie sind.Bist du bereit, die Feinheiten von KI-Agenten zu meistern und ihr volles Potenzial zur Automatisierung von Prozessen und zum Verkauf maßgeschneiderter Lösungen zu nutzen?Dann ist dieser Kurs für dich!Tauche ein in 'KI-Agenten: Automation & Business durch LangChain Apps'—wo du die grundlegenden und fortgeschrittenen Konzepte von KI-Agenten und LLMs, ihre Architekturen und praktischen Anwendungen erforschen wirst. Verändere dein Verständnis und deine Fähigkeiten, um die Führung in der KI-Revolution zu übernehmen.Dieser Kurs ist perfekt für Entwickler, Datenwissenschaftler, KI-Enthusiasten und alle, die an der Spitze der Technologie von KI-Agenten und LLMs stehen möchten. Egal ob du KI-Agenten erstellen, deren Automatisierung perfektionieren oder maßgeschneiderte Lösungen verkaufen möchtest, dieser Kurs bietet dir das umfassende Wissen und die praktischen Fähigkeiten, die du benötigst.Was dich in diesem Kurs erwartet:Umfassendes Wissen über KI-Agenten und LLMs:Grundlagen von KI-Agenten und LLMs: Einführung in KI-Agenten wie Autogen, Langchain, LangGraph, LangFlow, CrewAI, BabyAGI & deren LLMs (GPT-4o, Claude, Gemini, Llama & mehr).Tools und Techniken: Nutzung von LangChain, LangGraph und anderen Tools zur Erstellung von KI-Agenten.Function Calling und Vektordatenbanken: Verständnis von Function Calling und der Nutzung von Vektordatenbanken sowie Embedding-Modellen.Erstellung und Einsatz von KI-Agenten:Installation und Nutzung von Flowise mit Node: Schritt-für-Schritt-Anleitungen zur Installation und Nutzung von Flowise.Erstellung und Einsatz von KI-Agenten für verschiedene Aufgaben: Entwicklung von kreativen Schreibern, Social-
This course is designed to make you proficient in training and evaluating deep learning based object detection models. Specifically, you will learn about Faster R-CNN, SSD and YOLO models. For each of these models, you will first learn about how they function from a high level perspective. This will help you build the intuition about how they work. After this, you will learn how to leverage the power of Tensorflow 2 to train and evaluate these models on your local machine. Finally, you will learn how to leverage the power of cloud computing to improve your training process. For this last part, you will learn how to use Google Cloud AI Platform in order to train and evaluate your models on powerful GPUs offered by google. I designed this course to help you become proficient in training and evaluating object detection models. This is done by helping you in several different ways, including :Building the necessary intuition that will help you answer most questions about object detection using deep learning, which is a very common topic in interviews for positions in the fields of computer vision and deep learning.By teaching you how to create your own models using your own custom dataset. This will enable you to create some powerful AI solutions.By teaching you how to leverage the power of Google Cloud AI Platform in order to push your model's performance by having access to powerful GPUs.
In this course you'll learn about this new way of using LLM Agents: deploying multiple agents to work together as teams to accomplish more complex tasks for you!Everything is taught step by step and the course is fully practical with multiple examples and one complete AI Agents-based App that we build together.One of the things we use to accomplish this is ChatGPT's API so we can use ChatGPT through Python.We also use AutoGen to enable our Agents to work together and communicate with one another (to accomplish tasks with no human intervention).We also provide a few optional sections. One of these sections teaches to have a front-end, using Streamlit, to more easily interact with your AI Agents.Another optional section is for those who want to run AI Agents at scale! Here we show you how to deploy your LLM Agents on Google Cloud, so anyone can use your product.Lastly, one more optional section is available showing how to set up a payment system/subscription model using Stripe for those who want to monetize their AI Agents-based App!Everything is explained simply and in a step-by-step approach. All code shown in the course is also provided.Please not that the OpenAI API is not free, you will need to fund your OpenAI developer account with about $5-10 to follow through with the class and build your own app. We clearly show and explain how to do this and minimize your OpenAI costs during this class.
Este curso contém uso de inteligência artificial.Estamos vivendo a era da Inteligência Artificial Generativa, mas você sabe o que é IA Generativa e Agentes de IA?E o que vem a ser Large Language Models?E Small Language Models?Como o ChatGPT é capaz de fazer tudo que faz?Como estas tecnologias podem impactar seu trabalho e sua vida?Qual é o impacto ético e social destas tecnologias?Como eu posso usar estas tecnologias a meu favor?Como agentes de IA podem aumentar minha produtividade?Estas são algumas das centenas de dúvidas que muitas pessoas têm em relação a era da IA Generativa e Agentes de IA.Eu sei pois como professor e palestrante sobre IA Generativas, recebo muitas perguntas como essas.Neste curso, busco esclarecer, de forma simples e didática, o que são LLMs, o que é Inteligência Artificial Generativa, Agentes de IA e a aplicação prática dessas tecnologias.Mas o curso vai além, o curso também explica os principais termos relacionados, em uma linguagem clara e didática.Outro diferencial deste curso é a prática.O curso apresentará e demostrará diversas ferramentas de Inteligência Artificial Generativa, que você pode usar imediatamente, para criar:TextoImagensVídeosVoz (inclusive como clonar voz)MúsicaConteúdo Misto (apresentações, páginas web, documentos)Relatórios de PesquisaPesquisa AutomatizadaE muito mais!Importante: O curso apresentará diversas ferramentas, mas como um curso introdutório, o objetivo não é mostrar cada detalhe das ferramentas! O curso fará uma demonstração prática, do uso rápido de cada ferramenta, para que você entenda que est
THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH PYTORCH, H2O, KERAS & TENSORFLOW IN PYTHON!It is a full 5-Hour+ Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using one of the most important Python Deep Learning frameworks- PyTorch, H2O, Keras & Tensorflow. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical machine & deep learning using the PyTorch, H2O, Keras and Tensorflow framework in Python. This means, this course covers the important aspects of these architectures and if you take this course, you can do away with taking other courses or buying books on the different Python-based- deep learning architectures. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of frameworks such as PyTorch, Keras, H2o, Tensorflow is revolutionizing Deep Learning... By gaining proficiency in PyTorch, H2O, Keras and Tensorflow, you can give your company a competitive edge and boost your career to the next level.THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON BASED DATA SCIENCE!But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real-life data from different sources using data science-related techniques and producing publications for international peer-reviewed journals. 
A warm welcome to the Deep Learning for AI: Build, Train & Deploy Neural Networks course by Uplatz.Deep learning is a specialized branch of machine learning that focuses on using multi-layered artificial neural networks to automatically learn complex patterns and representations from data. Deep learning enables computers to learn and make intelligent decisions by automatically discovering the representations needed for tasks such as classification, prediction, and more—all by processing data through layers of artificial neurons.Deep learning is a subfield of machine learning that focuses on using artificial neural networks with many layers (hence “deep”) to learn complex patterns directly from data. It has revolutionized how we approach problems in image recognition, natural language processing, speech recognition, and more. Below is an overview covering how deep learning works, its key features, the tools and technologies used, its benefits, and the career opportunities it presents.Some of its key features are:Neural Networks at its CoreDeep learning models are built on neural networks that consist of multiple layers (hence "deep") of interconnected nodes or neurons. These layers process input data step-by-step, each extracting increasingly abstract features.Learning Hierarchies of FeaturesThe initial layers might capture simple patterns (like edges in an image), while deeper layers build on these to recognize more complex patterns (like shapes or even specific objects).Automatic Feature ExtractionUnlike traditional machine learning, where features are manually engineered, deep learning models learn to extract and combine features directly from raw data, which is particularly useful when dealing with large and unstructured datasets.ApplicationsThis approach is highly effecti
A professional certificate program from IBM that covers the fundamentals of AI engineering, including machine learning, deep learning, and AI ethics. It has a strong focus on practical, job-ready skills.
This course covers the fundamentals of tree-based models, including decision trees, random forests, and gradient boosting. You will learn how to build, tune, and evaluate these models using Python's scikit-learn library.
A lecture focusing on the role of optimization in machine learning, covering various algorithms and their properties.
A four-week course that explores the ethical and societal aspects of the increasing use of artificial intelligence technologies. The course aims to raise awareness and stimulate reflection and discussion upon the implications of AI in society, covering topics like algorithmic bias and surveillance.
This course explores the legal issues surrounding Artificial Intelligence, including intellectual property, legal risk, and data protection.
This course teaches you how to build end-to-end machine learning applications. It covers topics such as feature engineering, intermediate machine learning models, and recommender systems.
This Nanodegree program teaches how to build and orchestrate AI agents that can reason, plan, and use tools. A key project involves building an AI-powered project manager, providing hands-on experience in creating multi-agent systems.
A comprehensive Nanodegree program that covers the application of AI and machine learning to financial data for developing trading strategies. It includes significant coverage of time series analysis.
This online learning platform offers free courses and resources on data science and AI topics. The Neuro-Symbolic AI interest group aims to identify the foundations of this field, explore methods for integrating learning and reasoning, and identify applications in areas like robotics and commonsense reasoning.
This course includes a module on Ensemble Learning, covering decision trees and random forests.
Designed by teachers for teachers, this course bridges the gap between common beliefs about AI and its reality. It provides an overarching understanding of AI concepts and applications in education and how it can be embedded across the school curriculum.
Offered by the University of Amsterdam, this course covers the fundamentals of statistics, including descriptive statistics, probability, and inferential statistics.
Offered by the University of California, Santa Cruz, this course introduces the Bayesian approach to statistics, covering probability, data analysis, and the key differences from the Frequentist approach.
This course, part of the Deep Learning Specialization, focuses on convolutional neural networks (CNNs) and their application to computer vision tasks like image classification. You will learn to build and train CNNs and apply them to visual detection and recognition tasks.
This course introduces the importance of quality data in machine learning. It covers techniques to retrieve, clean, and apply feature engineering to data, preparing it for preliminary analysis and hypothesis testing.
A comprehensive exploration of how Generative Artificial Intelligence (GenAI) is revolutionizing the field of market research. The course offers an in-depth understanding of the capabilities of GenAI in this domain and provides practical strategies for leveraging these powerful tools in day-to-day market research activities.
This course provides insights into how Generative AI is revolutionizing user research and design thinking. It covers mastering AI tools for user research, synthesizing and interpreting research data, and applying AI to generate strategic insights.
Learn how to identify and fix common data quality issues in R, including missing values, outliers, and inconsistent data.
This course provides a comprehensive overview of data cleaning techniques in Python, from identifying and handling missing data to dealing with inconsistent data formats.
This course teaches the fundamentals of data wrangling and cleaning using Python and the Pandas library, essential skills for any data scientist.
This course teaches you how to use open-source models from the Hugging Face Hub for various tasks like NLP, audio, and image processing. You will learn to use the transformers library to perform these tasks with just a few lines of code and deploy your applications using Gradio and Hugging Face Spaces.
A comprehensive book on numerical optimization, covering both theory and practical algorithms.
A three-day bootcamp designed for technical users to explore the intersection of artificial intelligence and cybersecurity. The course covers AI threats, defense mechanisms, forensics, and incident response for AI systems.
This training shows how to integrate AI into various aspects of grant development efforts. It covers 20 ways to use AI to save time and produce better results, while also highlighting common mistakes to avoid.
A talk from the Spark/AI Summit about the Hopsworks Feature Store and its integration with Databricks. It explains how the feature store centralizes features for easier discovery, governance, and reuse.
An immersive series of live, hands-on workshops that blend design thinking with AI innovation. Participants learn to integrate AI tools for idea generation, research synthesis, and prototyping.
Ideal for business leaders, consultants, and functional leaders who want to strategically integrate AI and generative AI into their operations for growth and decision-making.
An interactive, half-day workshop on Artificial Intelligence essentials for Product Managers. It covers crafting a product vision, creating value hypotheses, and validating them using AI.
This video course equips you with the knowledge and skills needed to implement multimodal AI systems. You will gain hands-on experience building visual question and answering models, generating personalized images with diffusion, designing end-to-end multimodal applications, and fine-tuning multimodal models.
This training course empowers you to unlock the potential of Microsoft's cutting-edge language models. You will learn to provision the Azure OpenAI service, deploy powerful models, and use them to build groundbreaking AI applications that can write, chat, and understand human language.
A hybrid course that teaches how to analyze, forecast, and optimize energy demand using AI and data science with Python. It is designed for InnoEnergy masters students and PhD researchers, focusing on practical lab sessions and real-world energy use cases.
This course teaches how to use the Intel® VTune™ Profiler to analyze and optimize the performance of AI and machine learning workloads. It covers identifying performance bottlenecks in deep learning frameworks like TensorFlow and PyTorch.
An entry-level cybersecurity certification that covers fundamental concepts, including incident response. While not solely focused on AI, it provides a foundational understanding of security principles relevant to AI environments.
In this project-based course, students work to develop and evaluate a conversational system using a scientific approach, covering technologies like natural language processing and speech technology.
This course explores Vertex AI as a platform for enterprise-ready generative AI. Participants will learn to create search engines and chat applications using Vertex AI Search and Vertex AI Agents. The curriculum also covers integrating the Vertex AI Agent Builder into applications and productionizing the created search engines and chat applications, including managing changing data, security features, monitoring, and troubleshooting.
This course focuses on creating business value through the strategic implementation of AI. Participants gain an understanding of AI technologies and learn to identify business challenges and data sources to select the appropriate AI solutions.
This program provides a comprehensive understanding of how AI can drive sustainability initiatives. It is designed for professionals looking to apply AI technologies to environmental conservation, climate risk assessment, and sustainable development, covering topics like AI for climate modeling, renewable energy optimization, and environmental monitoring.
This certificate program is designed for experienced community managers looking to advance their careers by incorporating AI into their community management strategies. The course focuses on practical, real-world applications to enhance community engagement.
This course provides a comprehensive guide to the use of artificial intelligence in medical imaging, including key processes such as image segmentation, classification, feature extraction, and deep learning techniques. It is tailored for healthcare professionals, data scientists, AI developers, and medical students.
A course with 14 short lessons on how to use ChatGPT to optimize administrative tasks in healthcare outside of direct patient contact.
A free online course that introduces the theory and applications of machine learning algorithms with a focus on policy applications and issues. The course includes hands-on applications using R and Python.
This course provides practical guidance on using generative AI to update contract drafting skills. It covers topics such as using AI to analyze early research data, structure deals, and navigate the ethical considerations of AI-assisted legal practice.
A live, instructor-led training available online or onsite for professionals with intermediate knowledge. The course is designed for those looking to use machine learning and AI tools to automate and improve financial crime detection, compliance monitoring, and operational governance.
This instructor-led, live training (online or onsite) is aimed at public sector staff with limited or no prior experience using AI tools who wish to apply ChatGPT effectively for government-related communication, analysis, and administrative productivity.
An instructor-led, live training that teaches participants how to apply AI tools, computer vision, and machine learning techniques to automate inspections and improve product quality in manufacturing.
An instructor-led, live training course for IT professionals on the emerging field of AI TRiSM. The course aims to provide an understanding of the key concepts of AI trust, risk, and security management, how to identify and mitigate risks, and implement security best practices for AI.
An instructor-led, live training course (online or onsite) for intermediate-level developers and AI practitioners on using LoRA to efficiently fine-tune large-scale models, especially in resource-constrained environments.
This course introduces participants to Isaac Sim, a cutting-edge solution for robotics and simulation. You'll learn to develop simulation applications, specify scenes with USD components, and control robotic models within the simulation environment.
This is a comprehensive guide in the form of an eBook that explores the benefits of using data contracts to improve data quality in modern data platforms. It provides practical tips and best practices for implementing data contracts in an organization to build a true data-driven culture with a focus on accountability and governance.
A live event for data scientists and MLOps engineers to understand the relevance of feature stores in real-time ML. It provides hands-on experience using Feast to store, manage, and serve features.
This course provides a comprehensive exploration of the transformative role that AI plays in modern transportation systems. It covers topics like AI for Traffic Management and Optimization and Autonomous Vehicles.
An introductory course on the integration of AI in manufacturing to enhance production efficiency, reduce downtime, and improve product quality, covering predictive maintenance and quality control.
This course explores the transformative impact of AI on the fashion industry. It covers AI-driven fashion design, trend forecasting, manufacturing, and retail. Students will learn how AI is used to create innovative designs, predict future trends using big data, and improve efficiency in clothing production.
A free introductory course exploring the role of artificial intelligence in enhancing security measures against evolving cyber threats, covering topics from threat detection to incident response.
This certificate program equips professionals with the skills to turn data into actionable insights for strategic decision-making in global markets. It emphasizes ethical practices and the use of cutting-edge tools, including generative AI. The curriculum covers mastering the CI cycle, applying analytical frameworks, and leveraging AI tools for data collection and visualization to design actionable intelligence reports.
This course dives into the rapidly evolving field of Edge AI, highlighting its development, practical applications, and emerging trends. It explores how Edge AI is transforming various industries by enabling faster data processing and decision-making at the source.
An accredited program that explores AI's role in renewable energy, covering neural networks, deep learning, load forecasting, and predictive maintenance.
A free online certificate course for professionals looking to transition to AI-related opportunities with a business focus, covering AI's impact on various industries and career empowerment strategies.
This course introduces Quality 4.0, which transforms quality management using digitalization and artificial intelligence technologies, aligning quality practices with Industry 4.0.
A two-day online intensive course for real estate investors and professionals on applying AI for better decision-making. It explores tools for data analytics, predictive modeling, and automated valuation to identify, evaluate, and manage properties more effectively.
This program explores the application of AI in healthcare, covering ethical considerations and practical applications to improve patient care and industry transformation.
A free intermediate-level course covering linear algebra, calculus, and probability for machine learning, with an included certificate.
A comprehensive three-day virtual workshop designed for competitive intelligence professionals to harness the power of AI. The course covers foundational AI literacy, hands-on application of AI tools like ChatGPT and Claude.ai, crafting effective prompts, and building automated workflows. The workshop focuses on transforming AI-generated data into strategic narratives and provides frameworks for positioning CI professionals as indispensable strategic partners in the AI age.
This course introduces the theoretical foundations and algorithmic developments in stochastic optimization for machine learning. It covers basic convex optimization theories and focuses on stochastic approximation and its accelerations in statistical and machine learning models.
A free course that provides a comprehensive understanding of ensemble learning techniques, including bagging, boosting, and stacking.
A presentation exploring the state-of-the-art applications of AI in medical imaging through the lens of Responsible AI. It addresses critical ethical considerations such as bias mitigation, transparency, accountability, and data privacy, along with regulatory and implementation challenges.
A series of video lectures from Stanford University on how machine learning can be used for causal inference, including estimating treatment effects and designing targeted interventions.
A comprehensive course designed to equip learners with essential skills in artificial intelligence (AI) for the insurance industry, highlighting the importance of AI in identifying, assessing, and mitigating risks.
A webinar that provides a step-by-step demonstration on how to use AI as a personal assistant for research, writing, and editing grant proposals, without losing the human touch.
This course is designed for corporate professionals to apply AI knowledge to revolutionize quality assurance processes. It provides practical skills for implementing AI-driven quality control systems. The course is suitable for quality control managers, manufacturing supervisors, and quality assurance specialists.
A comprehensive program designed to provide the knowledge and skills needed to understand and implement face recognition technology. The course is intended for professionals such as computer vision engineers, software developers, and privacy and ethics advocates.
A training program designed to equip professionals with the skills to leverage artificial intelligence for optimizing cost and price analysis. The course explores cutting-edge AI techniques and tools to enhance decision-making and profitability.
A 2-day certification program for professionals to apply Artificial Intelligence (AI) in pharmaceutical manufacturing and quality assurance. The course covers predictive maintenance, real-time quality control with vision systems, and AI-powered Process Analytical Technology (PAT).
A step-by-step guide on how to integrate TensorFlow Lite with Flutter to build AI-powered mobile apps for both iOS and Android from a single codebase. It covers using pre-trained models and training custom models.
Gain insights into AWS Bedrock and LLMs to position yourself at the forefront of AI innovation. You will build and deploy three AI projects using foundation models from a single API.
This course teaches scientifically proven strategies, techniques, and skills used by top performers to learn more efficiently. It covers topics like memory, focus, and skill acquisition.
A comprehensive chapter on optimization algorithms for deep learning from the highly-regarded Deep Learning book.
A free, open-source guide to the fundamentals of prompt engineering for AI systems like ChatGPT and Claude, covering how to craft effective instructions for various professional applications.
This training covers both text classification and NER, explaining entity types and the differences between rule-based and machine learning approaches. It includes hands-on labs for implementing NER with spaCy and customizing models for specific applications.
This book provides a practical guide to the key concepts in statistics for data scientists. It covers topics such as exploratory data analysis, sampling, and hypothesis testing. The book is very hands-on and includes many examples using R and Python.
This global certificate course empowers fashion professionals and tech enthusiasts to leverage artificial intelligence in design, manufacturing, and retail. Students will explore machine learning algorithms for trend forecasting, personalized recommendations, computer vision for quality control and virtual try-on, and data analysis for optimized supply chains.
Learn TensorFlow Developer Professional Certificate
Learn Johns Hopkins Data Science Specialization
Learn University of Michigan Applied Data Science with Python
Harvard CS50 Introduction to Artificial Intelligence with Python. Learn graph search, optimization, machine learning, neural networks, and NLP.