The original Stanford ML course taught by Andrew Ng
Fast-paced introduction to machine learning using TensorFlow. Covers essential ML concepts with hands-on exercises and real-world examples.
Comprehensive deep learning specialization by Andrew Ng. Master neural networks, CNNs, RNNs, and modern deep learning architectures.
Complete natural language processing specialization covering transformers, attention mechanisms, and modern NLP techniques.
Master convolutional neural networks and modern computer vision architectures for image classification and object detection.
Complete data science workflow specialization covering data cleaning, analysis, visualization, and machine learning applications.
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.
A search result page on Coursera for courses related to optimization algorithms, offering a variety of options.
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 explores advanced Convolutional Neural Networks (CNNs), Transfer Learning, and Recurrent Neural Networks (RNNs). It delves into sophisticated architectures like VGG16 and their practical applications.
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.
For those with an intermediate to advanced understanding of computer vision, this course covers advanced topics like deep learning, convolutional neural networks (CNNs), object detection, image segmentation, and generative models. It is taught by a renowned expert in the field and is designed for students with a strong programming background.
This course covers advanced machine learning topics, including a detailed section on ensemble learning with decision trees, random forests, and gradient boosting.
Offered by Johns Hopkins University, this course equips learners with skills to combat advanced cybersecurity threats using artificial intelligence, focusing on malware detection and network anomaly identification.
This course delves into advanced machine learning techniques, including an in-depth look at ensemble learning methods like bagging, boosting, and stacking.
This course delves into more advanced regression topics, including generalized linear models, mixed-effects models, and survival analysis, using the R programming language.
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 focuses on unleashing the potential of AI systems by mastering Retrieval-Augmented Generation (RAG) techniques with Knowledge Graphs. You will learn to design, build, and query advanced Knowledge Graphs and integrate them with AI systems to boost contextual understanding and improve retrieval efficiency using tools like Neo4j and LangChain.
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.
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 application of Artificial Intelligence in the context of autonomous vehicles and robotics, covering key concepts and techniques in this rapidly growing field.
This specialization covers key AI concepts for cybersecurity including anomaly detection, generative AI, fraud detection, and intrusion prevention. Learners will gain skills in threat detection, cyber threat intelligence, and network analysis. The course is designed for beginners and can be completed in 3-6 months.
This course explores the application of AI techniques to enhance engineering design and optimization. It covers generative design, evolutionary algorithms, and topology optimization, providing a comprehensive introduction to using AI for design creativity and process streamlining.
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.
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 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 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.
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 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 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 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 explores the legal issues surrounding Artificial Intelligence, including intellectual property, legal risk, and data protection.
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.
This course includes a module on Ensemble Learning, covering decision trees and random forests.
An advanced course by IBM that covers feature engineering, data ethics, unsupervised learning, and dimensionality reduction. Students will learn about responsible AI, text mining, and data wrangling.
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 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 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.
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.
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.
This course provides a comprehensive introduction to attention mechanisms and the transformer models that are foundational to modern GenAI systems. It covers self-attention, multi-head attention, and the overall transformer architecture, with real-world demos.
A deep learning course that offers a comprehensive introduction to Autoencoders, Variational Autoencoders (VAEs), and Generative Adversarial Networks (GANs).
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.
This course provides an introduction to the engineering of autonomous aerospace systems, with a focus on drones. It covers topics such as modeling, simulation, and control of autonomous vehicles.
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.
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 Bayesian approaches to time series analysis, covering models like Autoregressive (AR) and Dynamic Linear Models (DLMs).
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.
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 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 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.
This course covers fundamental mathematical concepts for machine learning, including derivatives, integrals, and optimization techniques like gradient descent.
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.
This course from Columbia University provides an introduction to causal inference. You will learn about the key concepts in causal inference, such as confounding, selection bias, and instrumental variables. The course includes lectures, quizzes, and a final project.
A continuation of the Causal Inference course from Columbia University, this advanced course delves into topics like mediation, principal stratification, and longitudinal causal inference.
This course focuses on the theory and practice of various classification algorithms in machine learning.
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 course, part of the 'Advanced Machine Learning' specialization, delves into the practical aspects of machine learning competitions. It covers advanced feature engineering, ensembling methods, and other techniques used by top Kaggle competitors.
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 provides a comprehensive introduction to computer vision, covering topics like image processing, object detection, and the application of deep learning models in vision systems.
This course, taught by a leading expert, covers the fundamentals of convex optimization and its applications.
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.
Part of the DeepLearning.AI TensorFlow Developer Specialization, this course teaches best practices for using TensorFlow to build scalable AI-powered algorithms. You'll learn advanced techniques to improve computer vision models, including strategies to prevent overfitting like augmentation and dropout.
This course from the University of Pennsylvania provides a comprehensive introduction to causal inference, covering topics like potential outcomes, confounding, directed acyclic graphs (DAGs), matching, and instrumental variables.
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.
Part of the IBM Data Analyst Professional Certificate, this course covers the fundamentals of data analysis using Python, including working with data, exploratory data analysis, and an introduction to machine learning models.
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.
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 course covers advanced methods for data cleaning, preparation, and optimization using AI-assisted tools. You'll learn to generate synthetic data, address privacy concerns, and resolve data quality issues.
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 University of London course provides a practical introduction to the K-Means clustering algorithm, with a focus on the underlying statistical concepts.
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 teaches how to use PySpark for streaming data processing and Natural Language Processing (NLP) applications. It is aimed at data professionals who want to build scalable data-streaming applications and perform advanced NLP tasks on large datasets.
Offered by Duke University, this beginner-level course covers the foundational math skills needed for data science.
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.
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.
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.
This course covers the evaluation of Large Language Models, from foundational methods to advanced techniques using Vertex AI's tools like Automatic Metrics and AutoSxS. It is designed for AI Product Managers, Data Scientists, and AI Ethicists, and it explores the future of generative AI evaluation across different media.
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.
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.
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.
A comprehensive introduction to the mathematical principles that form the foundation of artificial intelligence and machine learning, bridging essential concepts with real-world AI applications.
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.
A course focused on acquiring practical expertise in using generative AI for fraud prevention and detection analytics.
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.
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 course empowers legal professionals to use Generative AI for creating high-quality contracts quickly and accurately while ensuring compliance. It covers the fundamentals of legal drafting, practical AI applications, and advanced techniques to streamline workflows and reduce errors.
This course delves into the advanced uses of generative AI for detecting fraud and ensuring compliance. Participants will learn how generative AI is transforming risk management and how to apply AI-based strategies.
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 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.
A beginner-friendly course exploring how Generative AI is transforming supply chain management, covering applications in demand forecasting, inventory optimization, and logistics through practical insights and case studies.
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.
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 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.
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.
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.
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.
This course provides an introduction to generative AI, covering topics like machine learning, virtual environments, and responsible AI.
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.
An intermediate-level course on leveraging Generative AI to improve customer success processes, engagement, and support automation, while addressing ethical and data privacy challenges.
Taught by AI experts at Google, this course introduces a 5-step framework for generating effective outputs from AI. It covers multimodal prompting, prompt chaining, and building reusable prompt libraries.
From Johns Hopkins University, this course focuses on the principles of hypothesis testing as applied to public health research questions.
This course provides practical skills in using Python and the scikit-learn library for machine learning, with a focus on supervised learning.
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 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 introduces advanced machine learning and NLP techniques for parsing and extracting information from unstructured text documents in healthcare, such as clinical notes and radiology reports.
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.
This beginner-level course introduces the exciting field of Computer Vision and its applications in various industries. You will learn about computer vision, its applications, and how to process images using Python, Watson AI, and OpenCV. The course also covers building image classification models and custom classifiers.
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 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 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 provides a comprehensive overview of how machine learning functions in embedded systems. It teaches students how to train neural networks and deploy them to microcontrollers, a field also known as TinyML. The course is designed for beginners with no prior machine learning experience, but some familiarity with Arduino and microcontrollers is recommended.
A beginner-friendly course that introduces you to GitHub Copilot, showing how to boost productivity, write smarter code, and integrate AI into your development workflow. You will learn to harness its capabilities for faster, error-free coding.
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.
A beginner-level micro-learning course that provides an overview of Large Language Models, including their architecture, frameworks, and applications. It also introduces fundamental NLP concepts.
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 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.
Learn the fundamentals of robotics and how to leverage AWS services for developing and deploying robotics applications.
This course provides an introduction to TensorFlow, one of the most popular deep learning frameworks. You'll learn the basics of building and training neural networks for computer vision tasks.
This course provides an introduction to the concepts of time series analysis, covering topics like stationarity, autocorrelation, and basic models.
This microlearning course provides a foundational understanding of Vertex AI, guiding learners through the platform's interface and its core components. The curriculum is designed to impart strategic insights into how Vertex AI can be effectively utilized in various projects. It is suitable for aspiring data scientists, machine learning enthusiasts, and professionals looking to leverage the power of AI. The course covers the basics of Vertex AI, its key features, and its role in the MLOps process.
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 teaches you how to leverage knowledge graphs in Retrieval-Augmented Generation (RAG) applications to enhance Large Language Model (LLM) performance with structured data relationships, advanced querying, and comprehensive information retrieval techniques. You will learn to build a knowledge graph from text documents and use it to improve the output of LLMs.
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.
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.
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 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 course on Coursera provides skills to optimize and deploy domain-specific large language models for advanced Generative AI applications. It covers supervised fine-tuning, parameter-efficient methods (PEFT), and reinforcement learning with human feedback (RLHF).
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.
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 is a non-technical introduction to the basics of machine learning, including supervised learning concepts.
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.
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.
The third course in the DeepLearning.AI specialization, focusing on the fundamentals of probability and statistics for machine learning and data science.
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.
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.
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.
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.
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 beginner-level course introduces the concepts of linear algebra that are relevant to AI, machine learning, and deep learning.
This course covers the mathematical foundations of optimization and its applications in data science.
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 provides a case-study based introduction to the foundational concepts of machine learning.
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 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 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 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.
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.
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.
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.
Learn about the framework of online learning, where algorithms make sequential decisions and learn from feedback.
Learn to formulate and solve various optimization models that are central to machine learning algorithms.
This course provides an in-depth look at the theory and methods of optimization.
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 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.
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 University of London course provides a comprehensive introduction to probability and statistics, focusing on understanding and interpreting p-values and confidence intervals.
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.
A focused course on logistic regression and other supervised machine learning techniques using Python.
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.
Part of the Applied Data Science with Python Specialization, this course provides an introduction to text mining and NLP. It covers understanding and manipulating text data in Python, including topic modeling and text classification.
This course focuses on optimizing machine learning workflows through efficient data handling and training techniques in PyTorch. It covers advanced DataLoader configurations, profiling tools, and modern optimization strategies like mixed precision training and gradient accumulation.
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.
The fifth course in the Google Advanced Data Analytics Certificate. You'll practice modeling variable relationships using methods such as linear regression, ANOVA, and logistic regression.
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.
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.
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.
Part of the IBM Advanced Data Science Professional Certificate, this course covers specialized modeling techniques, including time series analysis and survival analysis.
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.
This course is designed for scientists, engineers, and other problem-solvers who want to learn the basics of statistical thinking and how to apply it to real-world problems. You will learn about data analysis, experimental design, and statistical modeling.
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.
This course explores supervised learning techniques for marketing applications. The curriculum covers customer behavior analysis, product recommendation systems, and customer lifetime value prediction.
A course that focuses specifically on classification techniques within supervised learning, offered by IBM.
A course that focuses specifically on regression techniques within supervised learning, offered by IBM.
This is the first course in the Machine Learning Specialization. It provides a broad introduction to modern machine learning, including supervised learning (linear regression, logistic regression, neural networks, and decision trees). You will build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
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 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 covers time series analysis and mining techniques with a focus on practical applications in R.
This course provides a comprehensive introduction to time series analysis and forecasting, covering widely used techniques like ETS and ARIMA with hands-on examples in Python.
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.
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 course explores how to use data-driven AI to enhance customer engagement and build a competitive advantage. It's a beginner-level course that can be completed in about one week.
A course that covers the fundamentals of vector databases, from embeddings to practical applications. It is part of the DeepLearning.AI offerings on Coursera.
Part of a specialization by INSEAD, this course is taught by experts in how emerging technologies impact business and society. It covers topics such as smart contracts, different types of digital assets, and how trust is established in decentralized systems.
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.
A lecture focusing on the role of optimization in machine learning, covering various algorithms and their properties.
This professional certificate program from IBM is designed for those who want to take their data science skills to the next level. It covers advanced topics, including advanced machine learning, deep learning, and big data.
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 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 professional certificate from Google builds on foundational data analytics skills, focusing on advanced topics like statistical analysis, machine learning, and predictive modeling using Python and Tableau. It includes hands-on projects to prepare learners for senior data analyst and junior data scientist roles.
This program covers the fundamentals of cybersecurity, including identifying threats, securing networks, and using tools like Python, Bash, and Linux. It also includes training on AI in cybersecurity from Google experts. No prior experience is required.
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.
A comprehensive program by Google that equips learners with in-demand skills for project management, including Agile methodologies. The course now includes AI training from Google experts, teaching how to use AI for tasks like creating project charters, identifying risks, and improving communications. It is designed for beginners with no prior experience.
A comprehensive, beginner-level certificate program that covers the entire UX design process. It has been updated to include AI training from Google experts, teaching how to leverage AI in design.
A comprehensive 10-course program designed for beginners to become job-ready AI developers in about six months. It covers building AI-powered applications and chatbots using Python, Flask, and JavaScript, with no prior programming experience required.
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 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 beginner-friendly program that prepares for a career in cybersecurity, covering topics from network security to incident response and threat intelligence, with a focus on IBM tools.
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.
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.
An eight-course program designed to build job-ready skills in marketing analytics, including data collection, evaluation, visualization, and A/B testing. This certificate is suitable for beginners and covers the use of Meta Ads Manager.
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.
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 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.
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.
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.
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.
A project-based course where you will learn to build and evaluate classification trees using Python.
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.
A project-based course that teaches practical techniques for cleaning messy data in Microsoft Excel, including data manipulation and transformation.
A beginner-friendly project on creating a decision tree classifier using the R programming language.
A project that teaches you how to build popular ensemble methods like Bagging and AdaBoost from scratch in Python.
A guided project on Coursera that provides a hands-on introduction to essential causal inference techniques for data science.
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.
This course provides an introduction to AI image generation using Stable Diffusion, covering denoising techniques and advanced generative learning methods like autoencoders and contrastive learning.
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 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.
A 2-hour project-based course where you will learn to perform anomaly detection using PyCaret, a low-code machine learning library in 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 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.
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.
A guided project on Coursera that focuses on using the powerful BERT model for sentiment analysis tasks.
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.
This specialization covers advanced topics in machine learning, including more complex supervised learning models and techniques.
This specialization from Johns Hopkins University covers advanced statistical concepts, including mathematical statistics, regression models, and statistical inference, aimed at aspiring data scientists.
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 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.
This beginner-level specialization consists of three courses that equip finance professionals with AI literacy and practical skills. It covers the fundamentals of AI, its applications in finance, risk management, and how to use generative AI for tasks like financial forecasting and risk assessments.
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.
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 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 specialization examines how AI is reshaping the sports industry, with experts from Real Madrid C.F. sharing their direct application of technology in areas like athlete performance, injury prevention, and fan engagement.
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.
This specialization provides a deep dive into modern methods for analyzing time series and sequential data, with a focus on deep learning models and their applications.
This specialization from IBM experts teaches how to develop agentic AI systems using modern frameworks and workflow patterns. You'll work with LangGraph to create agents with memory and logic, explore self-improving agents, and design multi-agent systems with frameworks like CrewAI, AG2 (AutoGen), and BeeAI.
This specialization covers how data analytics is used to make business decisions and includes components of data exploration and visualization.
An intermediate to advanced specialization focused on C++ game development with Unreal Engine. It covers key AI programming concepts such as pathfinding and behavior trees.
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 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 covers the foundational concepts of data science, including data wrangling and visualization as part of the exploratory data analysis process.
A specialization that teaches how to create effective data visualizations and dashboards using Tableau, a key skill for exploratory data analysis.
This specialization provides a deep dive into data wrangling techniques using Python, including data collection, assessment, and cleaning, as well as handling missing values.
This specialization demonstrates how to use Excel for data analysis and visualization, which can be a powerful tool for initial data exploration.
Offered by Michigan State University, this specialization provides a thorough introduction to game design and development using Unity. It is a series of courses that includes hands-on projects where you build multiple 2D and 3D games.
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 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.
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 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.
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.
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 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 by Stanford University, taught by Andrew Ng, is a highly popular and comprehensive introduction to machine learning. It covers fundamental concepts including Support Vector Machines (SVMs) and kernel methods. The course is designed for beginners and provides a strong theoretical and practical foundation.
This specialization teaches how to use machine learning for tasks like demand forecasting and predicting product usage. It covers Python libraries for data manipulation and dives into advanced AI techniques like neural networks and random forests for supply chain challenges.
This is a 3-course specialization that provides a broad introduction to modern machine learning. It covers supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for AI and machine learning innovation.
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.
A specialization that guides learners through essential and advanced NLP techniques, from sentiment analysis and tokenization to neural translation and transformer models. It includes a course on 'Neural Models and Machine Translation'.
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.
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 specialization covering a range of optimization methods used in machine learning, from foundational concepts to advanced techniques.
This specialization from the University of Virginia and Boston Consulting Group provides a deep dive into the strategies and analytics behind effective pricing. It covers cost, customer value, and competition-based pricing strategies, with a focus on how to use data to make informed pricing decisions.
This specialization by Vanderbilt University covers prompt engineering from fundamentals to advanced skills. It teaches how to use large language models for various applications, including writing, summarization, and problem-solving, through a series of practical courses.
This specialization is a great starting point for beginners who want to learn Python for data science. While not focused solely on regression, it provides the necessary programming foundation to tackle more advanced machine learning courses.
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 comprehensive specialization covers the fundamental techniques of recommender systems, from non-personalized and content-based methods to collaborative filtering and advanced matrix factorization techniques. It is designed for both data mining experts and marketing professionals who want to gain a deeper understanding of these systems. The specialization includes a capstone project where you apply your knowledge to a real-world case study.
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.
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.
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