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Build agentic AI systems with iterative, multi-step workflows. Learn reflection, tool use, planning, and multi-agent patterns from AI pioneer Andrew Ng.

Free comprehensive course from Microsoft with 12+ lessons covering AI agent fundamentals. Learn frameworks like AutoGen, Semantic Kernel, and Azure AI Agent Service.

5-Day AI Agents Intensive Course with Google on Kaggle. Complete playlist with 5 video sessions covering hands-on intensive training to build advanced AI agent systems with Google's latest tools and frameworks.

Free 5-Day AI Agents Intensive course from Google on Kaggle, focused on vibecoding — building production AI agents through hands-on, AI-assisted coding workflows. Competition-format with Google's latest agent frameworks, daily challenges, and community leaderboard.

Learn to build AI agents with Hugging Face tools. Comprehensive course covering transformers, agent frameworks, and practical implementations for real-world applications.

In-depth research whitepaper on AI agent quality metrics and evaluation frameworks. Learn best practices for building and evaluating high-quality AI agents from Kaggle's research community.

Comprehensive guide on moving AI agents from prototype to production. Covers deployment strategies, scaling, optimization, monitoring, and maintaining production-ready agent systems.

Free official courses from Anthropic — the maker of Claude — covering Claude usage, prompt engineering, the Claude API, Claude Code, and AI fluency for teams. Self-paced, no cost, certificates of completion available.

Microsoft's free official training hub with thousands of hands-on learning paths and modules covering Azure AI, Copilot, .NET, Power Platform, and Microsoft Certifications. Sandbox environments included at no cost.

Free official learning platform from OpenAI — the maker of ChatGPT — with on-demand courses, live workshops, and expert sessions on ChatGPT, GPT models, prompt engineering, and real-world AI workflows for professionals and educators.

Free skill-building platform from IBM with 1,000+ courses on AI, cybersecurity, data analysis, and cloud computing. Includes IBM-issued digital credentials and badges that you can share on LinkedIn.

600+ free digital courses from Amazon Web Services covering cloud, generative AI, and machine learning. Includes learning plans and exam prep for AWS certifications such as AI Practitioner, Cloud Practitioner, and Solutions Architect.

Free hands-on courses from Hugging Face covering transformers and NLP, deep reinforcement learning, AI agents, audio, computer vision, and diffusion models. Built around the open-source Hugging Face ecosystem with practical notebooks and assignments.

Led by Andrew Ng, this graduate-level course provides a broad introduction to machine learning covering supervised learning, unsupervised learning, neural networks, SVMs, reinforcement learning, and applications in robotics and data mining. 20 lectures with comprehensive notes.

Natural Language Processing with Deep Learning by Chris Manning. Covers word embeddings, RNNs, transformers, LLMs, pre-training, post-training, and cutting-edge NLP research.

DataCamp's 4-hour intermediate course covering classification, regression, model evaluation, hyperparameter tuning, and preprocessing pipelines. Learn to predict customer churn, diabetes diagnosis, and more using real-world datasets.

MIT's introductory deep learning program covering neural networks, CNNs, RNNs, transformers, generative AI, and reinforcement learning. Hands-on labs in Google Colab with 100,000+ global students. Beginner-friendly with calculus prerequisites.

DataCamp's comprehensive 4-course track using PyTorch. Build models for image classification, text processing, and learn the Transformers architecture behind ChatGPT. Covers CNNs, RNNs, and pre-trained models.

MIT OpenCourseWare's hands-on deep learning course by Rama Ramakrishnan. Complete lecture videos and notes covering practical deep learning techniques with real-world applications.

DataCamp's 16-hour track covering LLM concepts, transformer architecture with PyTorch, Hugging Face integration, and building LLM applications with LangChain. Master techniques used in GPT-4 and Llama 3.

Complete video lecture series from Stanford's CS229 Machine Learning course (Autumn 2018) taught by Andrew Ng. Covers the mathematical theory behind ML algorithms for those aspiring to engage in theoretical research.

DataCamp's 12-hour track covering RL fundamentals, Deep Q-Networks, Policy Gradient methods, PPO, and RLHF for training LLMs. Includes hands-on projects in stock trading, robotics, and game AI.

DataCamp's 2-hour course on deploying ML models to production. Learn feature stores, experiment tracking, CI/CD pipelines, containerization, monitoring, and different MLOps maturity levels.

Under 5 hours to learn AI fundamentals, prompt engineering, and responsible AI usage. Created by Google AI experts with hands-on exercises for real workplace scenarios. Earn a Google certificate upon completion.

Free 1.5-hour Google Cloud course on Gemini multimodal applications, prompt design, model tuning, and the prompt-to-production lifecycle. Includes hands-on labs and skill badge option.

Free 1.25-hour skill badge course covering image recognition, NLP, image generation using Google's Gemini and Imagen models. Deploy applications on Vertex AI platform. Available in 9 languages.

Harvard's legendary introduction to computer science. Free course with lectures, problem sets, and certificates.

Harvard's CS50 variant for business professionals via edX. Covers computational thinking, internet technologies, web development, and cloud computing — designed for tech literacy without deep programming. Free to audit, taught by David J. Malan.

Advanced Stanford lecture on transformer-based models, covering attention approximation, position embeddings (sinusoidal and RoPE), BERT and derivatives, and key architectural modifications.

Expert guidance on building and advancing your career in artificial intelligence. Learn about career paths, skill development, industry trends, and strategies for success in the AI field.

Theory meets algorithms. A comprehensive textbook covering the theoretical foundations of machine learning with rigorous mathematical treatment and practical algorithmic insights.

Linear algebra to calculus made intuitive. Covers the essential mathematical foundations — linear algebra, analytic geometry, matrix decompositions, probability, and optimization — needed for ML.

The theory behind the code. Dive deep into the mathematical analysis that powers machine learning algorithms, from convergence proofs to generalization bounds.

Neural networks explained clearly. A structured approach to understanding deep learning from fundamental principles, covering architectures, training methods, and modern techniques.

From neurons to backpropagation. Covers neural network fundamentals, learning algorithms, and network architectures with clear explanations of how networks learn from data.

Graph Neural Networks and modern architectures. Explore how deep learning applies to graph-structured data, covering GNNs, message passing, graph transformers, and real-world applications.

Complexity and optimization theory. Understand the algorithmic foundations of ML including computational complexity, optimization methods, and efficient learning algorithms.

Statistical foundations with examples. Build a strong understanding of probability theory essential for machine learning — from distributions and random variables to Bayesian inference.

Beginner-friendly with real-world applications. An accessible introduction to probability concepts with practical examples, perfect for those starting their ML journey.

Statistical learning for production systems. Advanced methods for data analysis covering regression, classification, model selection, and techniques used in real production ML systems.

Comprehensive resource for preparing for machine learning interviews. Covers key concepts, common questions, and practical tips to ace your ML engineering interviews.

By Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar. Rigorous mathematical treatment of ML foundations covering PAC learning, Rademacher complexity, boosting, and kernel methods.

A modern and comprehensive textbook covering neural networks, backpropagation, CNNs, transformers, GANs, diffusion models, and reinforcement learning with interactive notebooks.

Covers decision making under uncertainty with practical algorithms. Explores optimization, probabilistic reasoning, sequential decision problems, and multi-agent systems.

By Sutton and Barto. The definitive textbook on reinforcement learning covering multi-armed bandits, MDPs, temporal-difference learning, policy gradient methods, and deep RL.

Comprehensive guide to building production ML systems. Covers system design, data pipelines, model serving, monitoring, and the full lifecycle of deploying ML at scale.

By Goodfellow, Bengio, and Courville. The classic deep learning textbook covering linear algebra, probability, numerical computation, neural networks, CNNs, RNNs, and generative models.

MIT Press open access monograph exploring the distributional perspective on RL, where agents learn the full distribution of returns rather than just expected values.

Comprehensive resource on MARL covering cooperative, competitive, and mixed multi-agent environments, game theory foundations, and modern deep MARL algorithms.

MIT Press open access monograph exploring the role of AI agents in long-term AI development, covering autonomous systems, decision-making, and the future of AI agent architectures.

Exploring limitations and opportunities in fairness-aware ML. Covers bias detection, fairness metrics, algorithmic fairness, and building equitable AI systems.

Former Tesla AI Director and OpenAI co-founder sharing deep dives into neural networks, GPT internals, and building AI from scratch. Essential viewing for understanding how modern AI actually works.

Statistics and machine learning concepts explained clearly with fun illustrations. Covers everything from basic statistics to neural networks, decision trees, PCA, and more — perfect for building strong ML foundations.

Beautiful visual explanations of mathematics, linear algebra, calculus, and neural networks. Grant Sanderson's animations make complex mathematical concepts intuitive — a must-watch for understanding the math behind AI.

AI researcher and author of "Machine Learning with PyTorch and Scikit-Learn" sharing tutorials on deep learning, LLMs, research paper walkthroughs, and practical machine learning engineering tips.

By Aurelien Geron. The go-to practical guide for ML engineers covering end-to-end projects with scikit-learn, deep learning with Keras and TensorFlow, CNNs, RNNs, GANs, and reinforcement learning with hands-on code examples.

By James, Witten, Hastie, and Tibshirani. A widely used textbook for learning statistical learning methods including regression, classification, resampling, tree-based methods, SVMs, and unsupervised learning with R and Python labs.

By Andriy Burkov. A concise yet comprehensive overview of machine learning covering supervised and unsupervised learning, neural networks, and best practices — perfect for quick reference and exam preparation.

By Andrew Ng. A practical guide focused on structuring ML projects, diagnosing errors, and making strategic decisions. Learn how to set up dev/test sets, handle bias and variance, and build effective ML pipelines.

By Christopher M. Bishop. A comprehensive textbook covering probability distributions, linear models, neural networks, kernel methods, graphical models, and approximate inference — the gold standard for ML theory.