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CourseraIntermediate

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.

UdemyBeginner

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.Welcome to TensorFlow 2.0!What an exciting time. It's been nearly 4 years since TensorFlow was released, and the library has evolved to its official second version.TensorFlow is Google's library for deep learning and artificial intelligence.Deep Learning has been responsible for some amazing achievements recently, such as:Generating beautiful, photo-realistic images of people and things that never existed (GANs)Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)Self-driving cars (Computer Vision)Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)Even creating videos of people doing and saying things they never did (Deep Fakes - a potentially nefarious application of deep learning)TensorFlow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.In other words, if you want to do deep learning, you gotta know TensorFlow.This course is for beginner-level students all the way up to expert-level students. How can this be?If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.Along the way, you will learn about

UdemyBeginner

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!This course is designed for ML practitioners who want to enhance their skills and move up the ladder with Deep Learning!This course is made to give you all the required knowledge at the beginning of your journey so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips, and tricks you would require to work in the Deep Learning space.It gives a detailed guide on TensorFlow and Keras along with in-depth knowledge of Deep Learning algorithms. All the algorithms are covered in detail so that the learner gains a good understanding of the concepts. One needs to have a clear understanding of what goes behind the scenes to convert a good model to a great model. This course will enable you to develop complex deep-learning architectures with ease and improve your model performance with several tips and tricks.Deep Learning Algorithms Covered:1. Feed Forward Networks (FFN)2. Convolutional Neural Networks (CNNs)3. Recurring Neural Networks (RNNs)4. Long Short-Term Memory Networks (LSTMs)5. Gated Recurrent Unit (GRUs)6. Autoencoders7. Transfer Learning8. Generative Adversarial Networks (GANs)Our exotic journey will include the concepts of:1. The most important concepts of TensorFlow and Keras from very basic.2. The two ways of model building i.e. Sequential and Functional API.3. All the building blocks of Deep Learning models are explained in detail to enable students to make decisions while training their model and improving model performance.4. Hands-on learning of Deep Learning algorithms from the beginner

UdemyBeginner

Welcome to the Captivating World of LLM Prompt Engineering!This course empowers you to unlock the true potential of Large Language Models (LL Ms), regardless of your experience level. Whether you're a seasoned professional or a curious beginner, this comprehensive program equips you with the skills to become a master of LLM prompt engineering.Master the Art of Crafting Powerful Prompts:Diverse Task Applications: Craft effective prompts tailored to various tasks, including generating informative summaries, creating captivating stories, or even translating languages, all through the power of well-designed prompts.Advanced Techniques Exploration: Move beyond the basics and delve into advanced concepts like iterative prompting, where you refine your prompt based on the LLM's initial output. Additionally, explore few-shot learning, allowing you to achieve impressive results even with limited data.Core LLM Concepts Demystified: Gain a solid understanding of fundamental LLM properties like statelessness and quantization. Explore how these properties impact prompt design and LLM behavior. Learn to identify and mitigate potential hallucinations in LLM outputs.Unleash LLM Capabilities Through Hands-on Learning:Code Walkthroughs Deepen Understanding: Go beyond theory with interactive code walkthroughs using Lamma 2 as a platform. Actively explore code examples to gain practical experience in setting up, configuring LL Ms, working with advanced models (e.g., quantized models), and leveraging specialized notebooks like AWQ for optimized workflows.Real-World Applications Solidify Skills: This course emphasizes the practical application of LLM prompt engineering. Learn how to tailor prompts to solve specific real-world problems, ensuring accurate and creative AI outputs. Translate your newfound knowledg

UdemyBeginner

Generative AI: From Fundamentals to Advanced Applications This comprehensive course is designed to equip learners with a deep understanding of Generative AI, particularly focusing on Large Language Models (LL Ms) and their applications. You will delve into the core concepts, practical implementation techniques, and ethical considerations surrounding this transformative technology.What You Will Learn:Foundational Knowledge: Grasp the evolution of AI, understand the core principles of Generative AI, and explore its diverse use cases.LLM Architecture and Training: Gain insights into the architecture of LL Ms, their training processes, and the factors influencing their performance.Prompt Engineering: Master the art of crafting effective prompts to maximize LLM capabilities and overcome limitations.Fine-Tuning and Optimization: Learn how to tailor LL Ms to specific tasks through fine-tuning and explore techniques like PEFT and RLHF.RAG and Real-World Applications: Discover how to integrate LL Ms with external knowledge sources using Retrieval Augmented Generation (RAG) and explore practical applications.Ethical Considerations: Understand the ethical implications of Generative AI and responsible AI practices.By the end of this course, you will be equipped to build and deploy robust Generative AI solutions, addressing real-world challenges while adhering to ethical guidelines. Whether you are a data scientist, developer, or business professional, this course will provide you with the necessary skills to thrive in the era of Generative AI.Course Structure:The course is structured into 12 sections, covering a wide range of topics from foundational concepts to advanced techniques. Each section includes multiple lectures, providing a comp

UdemyBeginner

Welcome to the 10 Days of Prompt Engineering, Generative AI, and Data Science Course Get hands-on with Prompt Engineering, Generative AI, and Data Science in just 10 days. I’m Diogo, and I’ve structured this course to take you from basics to advanced topics quickly. We’ll cover live sessions, hands-on labs, and real-world projects—all in 14 hours and 30 minutes of published video content. You’ll also receive lifetime updates so your learning never goes stale.You will build a portfolio of project on topics like:Prompt Engineering Fundamentals: Understand transformers, attention mechanisms, and how to structure prompts for optimal performance.Generative AI Workflows: Master tools like Google Colab, Jupyter Notebook, LM Studio, and learn how to fine-tune system messages and model parameters.OpenAI API for Text & Images: Integrate the OpenAI API into Python projects, explore parameters for better text generation, and tap into image generation (coming soon).Machine Learning with XG Boost & Random Forest: Explore advanced ML topics, including parameter tuning, SHAP values, and real-world approaches to customer satisfaction modeling.AI Agents with CrewAI: Dive into the next wave of AI automation (coming in Q1 2025).COURSE BREAKDOWN Introduction Meet your instructor, download course materials, set up your environment (Google Colab, Jupyter Notebook, RStudio).Preview the core projects we’ll tackle.Day 1 – Basics of Prompt Engineering Learn about transformers, attention, and chain-of-thought prompting.Experiment with LM Studio to practice

Learn 10 FREE AI Courses for Absolute Beginners in 2025

Learn 10-601: Introduction to Machine Learning

UdemyBeginner

This 200+ day globally recognized, industry-focused bootcamp is your all-in-one training for mastering Artificial Intelligence, Data Science, Machine Learning (ML), Deep Learning (DL), and Generative AI (GenAI)  from beginner to expert level with simplicity and depth. Designed for aspiring data scientists, software engineers, AI professionals, and innovation leaders, this course offers a blend of foundational theory, programming practice, machine learning applications, and real-world project. The curriculum aligns with current global AI trends and industry hiring standards.Whether you're targeting top roles in global tech firms, launching an AI-powered startup, or aiming to build a strong data science portfolio this bootcamp ensures you stay ahead in the global AI race.Core Modules (SEO Keywords: Data Science, Python, AI, Machine Learning, Generative AI)Data Science Fundamentals Data Science Sessions Part 1 & 2 – Foundation of modern data science methodologies and approaches.Data Science vs Traditional Analysis – Comparing data science techniques with conventional statistical methods.Data Scientist Journey Parts 1 & 2 – Skills, roles, and global career pathways.Data Science Process Overview – End-to-end project lifecycle and workflows.Programming Essentials (Python & R for Data Science)Introduction to Python for Data Science – Syntax, structures, and data analysis workflows.Python Libraries: Numpy, Pandas, Matplotlib, Seaborn – Building blocks for data processing and visualization.Introduction to R – Fundamentals of R programming for statistics and machine learning.Data Structures and Functions – Hands-on practice in Python & R for real-world data operations.Data Collection & Preprocessing Methods of Data Collection – Surveys, A

UdemyBeginner

This is an ambitious course. The goal here is simple: Only teach what you need to know for day 1 of your first data science job. No fluff, nothing out of context, no topics that are not relevant to real world applications. We will cover EVERY core topic and tool required for those new to data science: Python, R, SQL, Useful Math/Stats/Algorithms, Tableau, and Excel in depth. The course will cover skills that align with three different job types:- Data Analyst- General Data Scientist- Machine Learning Engineer You can expect to learn from first principles the foundational topics and tools used in practice today. We will avoid topics that are not useful or are simply too advanced when starting out. Your journey will be guided by the Data Science Road Map, a collection of the best resources gathered through years of experience by the instructor.In addition, we will survey every important technology required on the job including Git Hub, Kaggle, the basics of cloud, web development and docker. With over 200 videos, readings, and assignments, you can be sure you will be well prepared to join the data community.If you are just getting started or want to fill in some of your knowledge gaps this course is for you!

UdemyAdvanced

This Online Bootcamp is a compact and accelerated version of our 400-hour in-person master's program.It has four parts:- In Part 1, you will learn the keys to Artificial Intelligence and the new Generative AI, as well as its potential to revolutionize businesses, startups, and employment.- In Part 2, you will learn to build professional-level LLM Applications, the most potential applications of Generative AI. You will also learn how to build Advanced RAG LLM Apps, Multimodal LLM Apps, AI Agents, Multi-Agent LLM Apps, and how to manage LLM Ops.- In Part 3, you will learn how to build traditional and Gen AI apps without coding using Cursor AI and the new AI Coding Assistants. You will learn what are AI Coding Assistants like Cursor AI, Claude AI, v0, o1, Replit Agent, etc, and how to increase their performance by combining them with tools like the Replit platform, simplified backends like Firebase, Replicate AI, Stable Fusion, or Deepgram.- In Part 4, you will learn how to create SaaS applications without coding using Cursor AI. You’ll also see, through two high-level real-world examples, how Generative AI is transforming the SaaS (Software as a Service) model.By the end of this program, you will know how to do the following:AI AND BUSINESS Know the businesses that AI puts at risk of disappearing.Know the new opportunities created by AI for businesses.Design a plan to introduce AI into your company.Select an appropriate pilot project to introduce AI into your company.Form the first AI team in your company.Prepare your company's AI strategy.AI AND STARTUP Identify 100 opportunities to create AI startups.AI AND EMPLOYMENT Know the professions that AI puts at risk of disappearing.Know the new p

UdemyAdvanced

226 ChatGPT Prompts: A-Z ChatGPT Prompt Engineering Boot Camp Hey there, it's me, your next big career move. If you've ever thought, "How can I leverage the power of ChatGPT to elevate my game in my profession?", then this course is your answer. We're not just talking about a few tips here and there; this is the ultimate guide, the A-Z, the whole Bootcamp!Here's what you're getting:226+ ChatGPT Prompts tailored for various professions and life scenarios. Whether you're in logistics, HR, teaching, or even looking for a job, we've got you covered.Real-World Use Cases & Practice Exercises: Don't just learn, do. Apply what you learn in real-time, see the results, and iterate.Exclusive Access to Our Comprehensive ChatGPT Book: All the prompts we discuss? They're in there. A handy reference for whenever you need it.Stay Updated: This isn't a one-and-done deal. The course is updated with the latest from ChatGPT, including plugins, DALL·E 3, advanced analytics, and more.Diverse Categories: From e Commerce and content creation to health, fitness, and even travel. We've thought of everything, and then some.Why This Course?Look, the digital age is here, and it's not waiting for anyone. ChatGPT is revolutionizing how we work, communicate, and even learn. But here's the thing: knowledge without application is just trivia. This course ensures you apply what you learn, making you more efficient, effective, and, quite frankly, indispensable in your field.A Word from Your Instructor:I genuinely care about your growth. I'm not here to sell you a dream. I'm here to give you the tools to build that dream. This course? It's one

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.

UdemyIntermediate

Solve Business Problems Using Data Science Practically. Learn To Build &amp; 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.<

UdemyIntermediate

In This Course, Solve Business Problems Using Data Science Practically. Learn To Build &amp; 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

AWSIntermediate

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.

Learn 99% of Beginners Don't Know the Basics of AI

Learn 99% Of People STILL Don't Know The Basics Of Prompting (ChatGPT, Gemini, Claude)

CourseraBeginner

This course from the University of Pennsylvania provides a comprehensive introduction to causal inference, covering topics like potential outcomes, confounding, directed acyclic graphs (DA Gs), matching, and instrumental variables.

UdemyBeginner

Course Contents Deep Learning and revolutionized Artificial Intelligence and data science.&nbsp; Deep Learning teaches computers to process data in a way that is inspired by the human brain.This is complete and comprehensive course on deep learning. This course covers the theory and intuition behind deep learning models and then implementing all the deep learning models both in PyTorch and TensorFlow.Practical Oriented explanations Deep Learning Models with implementation both in PyTorch and TensorFlow.No need of any prerequisites. I will teach you everything from scratch.Job Oriented Structure Sections of the Course· Introduction of the Course· Introduction to Google Colab· Python Crash Course· Data Preprocessing· Regression Analysis· Logistic Regression· Introduction to Neural Networks and Deep Learning· Activation Functions· Loss Functions· Back Propagation· Neural Networks for Regression Analysis· Neural Networks for Classification· Dropout Regularization and Batch Normalization· Optimizers· Adding Custom Loss Function and Custom Layers to Neural Networks· Convolutional Neural Network (CNNs)· One Dimensional CNNs· Setting Early Stopping Criterion in CNNs· Recurrent Neural Network (RNNs)· Long Short-Term Memory (LSTMs) Network· Bidirectional LSTMs· Generative Adversarial Network (GANs)· DCGA Ns· Autoencoders· LSTMs Autoencoders· Variational Autoencoders· Neural Style Transfer· Transformers· Vision Transformer· Time Series Transformers. K-means Clustering. Principle Component Analysis.&nbsp; Deep Learning Models with implementation both in PyTorch and TensorFlow.

UdemyBeginner

This course is designed by an industry expert who has over 2 decades of IT industry experience including 1.5 decades of project/ program management experience, and over a decade of experience in independent study and research in the fields of Machine Learning and Data Science.The course will equip students with a solid understanding of the theory and practical skills necessary to learn machine learning models and data science.When building a high-performing ML model, it’s not just about how many algorithms you know; instead, it’s about how well you use what you already know.Throughout the course, I have used appealing visualization and animations to explain the concepts so that you understand them without any ambiguity.This course contains 9 sections:&nbsp; &nbsp; &nbsp;1. Introduction to Machine Learning&nbsp; &nbsp; &nbsp;2. Anaconda – An Overview &amp; Installation&nbsp; &nbsp; &nbsp;3. Jupyter Lab – An Overview&nbsp; &nbsp; &nbsp;4. Python – An Overview&nbsp; &nbsp; &nbsp;5. Linear Algebra – An Overview&nbsp; &nbsp; &nbsp;6. Statistics – An Overview&nbsp; &nbsp; &nbsp;7. Probability – An Overview&nbsp; &nbsp; &nbsp;8. OO Ps – An Overview&nbsp; &nbsp; &nbsp;9. Important Libraries – An Overview This course includes 20 lectures, 10 hands-on sessions, and 10 downloadable assets.By the end of this course, I am confident that you will outperform in your job interviews much better than those who have not taken this course, for sure.

UdemyIntermediate

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 Git Lab and push your code in Git Lab.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.

UdemyIntermediate

A-Z™ | TensorFlow ile Derin Öğrenme Kursumuzda klasik ve derin öğrenme tabanlı yöntemlerini kullanarak sınıflandırma nasıl yapıldığını öğrenip, TensorFlow kütüphaneleriyle gerçek hayat projeleri yapacağız.Projelerle Yapay Zeka ve Bilgisayarlı Görü Kursu İçeriği Giriş BölümüDerin Öğrenme Teori Derin Öğrenme Nedir Yapay Sinir AğlarıAktivasyon FonksiyonlarıOptimizasyon AlgoritmalarıLoss (Kayıp) FonksiyonlarıDerin Öğrenme TeoriCNN (Convolutional Neural Networks) Teori Evrişim İşlemiCNN (Convolutional Neural Networks)Piksel Ekleme (Padding)Adım Kaydırma (Stride)Ortaklama (Pooling)Ek Teori Epoch ve Batch Size Dropout Early Stopping Learning Rate TensorFlow ile Derin Öğrenme TensorFlow Temelleri Veriyi Hazırlama Model Oluşumu Sequential Model Egitimi Model Testi | 1. Kısım Model Testi | 2. Kısım Modeli Kaydetme/Yükleme - Save/Load Model Sonuçlarını Görselleştirme Modelin Ara Katmalarını Görselleştirme Functional Bir Model Oluşturma Callbacks | 1. kısım Callbacks | 2. kısım Data Augmentation - Veri Arttırma | 1. Kısım Data Augmentation - Veri Arttırma | 2. Kısım Transfer Learning - VGG Hazır Model Kullanma - VGG TensorFlow ile Trafik İşaretlerini Sınıflandırma Veriyi Hazırlama Model Eğitimi ve Test Real Time'da Test TensorFlow'da Weights &amp; Biases (WandB) | Özel Veri Wandb ile Keras'da Temel Fonsiyonlar Wandb ile Keras'da Sweepler Wandb ile Keras'da Sweep - Bonus Video TensorFlow Lite - Android App - Object detection - İmage Classification Efficient Det Lite Model Eğitimi - Object detection Efficient Det Lite Modeli Android'de Çalıştırma 1 - Object detection Efficient Det Lite Modeli Androi

This Udacity course, developed by Google, provides a practical introduction to A/B testing. You will learn how to design and analyze A/B tests. The course covers topics such as metrics, sample size, and statistical significance.

DatacampIntermediate

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.