31 resources · 28 free
Machine Learning
Train and evaluate models — ML fundamentals, deep learning, and applied model building.
Build a Computer Vision App with Azure Cognitive Services (Coursera GP)
Coursera Guided Project: build a computer vision app with Azure Cognitive Services — image analysis, object detection, face recognition, OCR via Vision APIs.
Breast Cancer Prediction Using Machine Learning (Coursera GP)
Coursera Guided Project: breast cancer prediction using machine learning — dataset analysis, classification models, evaluation.
Diabetes Prediction with PySpark MLlib (Coursera GP)
Coursera Guided Project: diabetes prediction with PySpark MLlib — distributed ML pipeline, feature engineering, model training and evaluation.
Graduate Admission Prediction with PySpark ML (Coursera GP)
Coursera Guided Project: graduate admission prediction with PySpark ML — regression on admission data, model building and evaluation.
Google Machine Learning Crash Course
Official Google course introducing machine learning with TensorFlow. Learn core concepts like linear regression, classification, neural networks and model evaluation through hands-on exercises with real datasets. Self-paced lessons combine video lectures, interactive visualizations and quizzes. Free for everyone with no prerequisites beyond basic programming and math. Backed by Google's ML education team and used by thousands of learners worldwide.
fast.ai Practical Deep Learning for Coders
Free course by Jeremy Howard and Rachel Thomas that teaches deep learning top-down: you build and train real models from lesson one before studying theory. Covers computer vision, NLP, tabular data and recommendation systems with PyTorch. Includes recorded lectures, Jupyter notebooks and a large community forum. Trusted by tens of thousands of learners and famous for making deep learning accessible.
Hugging Face Learn
Free official courses from Hugging Face covering NLP, computer vision, audio and deep reinforcement learning. The NLP course teaches transformer models with practical hands-on notebooks using the Transformers library. Each course includes video lessons, code examples and community discussion. Built for learners with some Python and ML basics. Run by the team behind the most widely used open-source ML platform.
Kaggle Learn Micro-Courses
Collection of free short micro-courses on Kaggle covering Intro to Machine Learning, Intermediate Machine Learning, feature engineering, deep learning and more. Each course combines short lessons with interactive exercises that run in-browser with real datasets. Includes shareable certificates of completion. Ideal for beginners wanting a fast practical start. Backed by Kaggle, the world's largest data science community.
Machine Learning Specialization (Andrew Ng)
Andrew Ng's Machine Learning Specialization, the most famous ML course ever created, now updated with Python. Covers supervised learning, unsupervised learning, advanced algorithms and practical advice for real projects. The full course is free to audit; graded assignments and certificates require paid enrollment. Designed for beginners with basic Python knowledge. Taught by Andrew Ng with millions of enrolled learners.
Elements of AI
Free introductory course on artificial intelligence created by the University of Helsinki and Reaktor. Covers what AI is, how it is used, and its societal implications in plain language. No programming or math background is required, making it perfect for complete beginners. Available in many languages and completed by over a million people. Widely used for AI literacy training in companies and schools.
mlcourse.ai Open Machine Learning Course
Free and open-source machine learning course by Yury Kashnitsky and the OpenDataScience community. Ten sessions cover classical ML with Python: EDA, decision trees, random forests, gradient boosting, clustering and time series. Includes theory lectures, coding assignments, Kaggle-style competitions and forum support. Available in English and Russian. An excellent bridge from theory to real-world data science.
Hands-On Machine Learning (Geron) GitHub
Official GitHub repository for Aurelien Geron's book Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow. Contains Jupyter notebooks with all code examples from the book, ready to run in Colab. Covers classical ML, neural networks, CNNs, RNNs, transformers and deployment. Completely free to browse and use. One of the most-starred machine learning repositories on GitHub.
scikit-learn User Guide and Tutorials
Official user guide and tutorials for scikit-learn, the standard Python machine learning library. Includes a quick start, tutorials on model selection, preprocessing, metrics and more. Step-by-step examples with code for every algorithm in the library. A perfect reference for beginners and working practitioners alike. Maintained by the scikit-learn developer community.
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PyTorch Tutorials
Official tutorials from the PyTorch team covering tensors, autograd, neural networks, computer vision and NLP. Includes beginner tutorials, deep learning recipes and advanced topics like distributed training and quantization. Notebooks run in Google Colab with one click. Free, current and updated with every PyTorch release. The primary learning resource for the PyTorch ecosystem.
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TensorFlow Learn
Official learning hub for TensorFlow with tutorials, guides and courses for every level. Covers Keras basics, computer vision, NLP, generative models and production ML. Includes interactive Colab notebooks and a structured learning path from beginner to expert. Maintained by Google's TensorFlow team. Free and updated alongside every TensorFlow release.
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DeepLearning.AI Courses
Education platform founded by Andrew Ng offering courses on deep learning, LLMs, MLOps and AI for everyone. Some short courses are free; specialization certificates require payment on Coursera. Courses include hands-on labs and are taught by world-leading AI instructors. Known for the Deep Learning Specialization and short courses with partners like OpenAI and LangChain. Popular with professionals upgrading their AI skills.
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3Blue1Brown Neural Networks Series
Animated video series by Grant Sanderson explaining neural networks and deep learning visually. Covers backpropagation, gradient descent and the math behind learning with stunning 3D animations. Widely considered the best conceptual introduction to how neural networks work. Free on YouTube and viewed by millions. A perfect companion before or during any ML course.
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StatQuest with Josh Starmer
YouTube channel by Josh Starmer explaining statistics and machine learning with clear, humorous animations. Covers regression, decision trees, neural networks, gradient boosting, PCA and much more. Episodes are short, jargon-free and build intuition first. Free and widely recommended by data science learners and instructors. Hundreds of videos across statistics and ML topics.
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Papers with Code
Platform connecting machine learning papers with their code implementations and benchmark results. Browse thousands of papers across vision, NLP, RL and more, each with linked repositories and leaderboards. Track state-of-the-art results on major benchmarks. Free to browse and contribute. Maintained by Meta and the research community.
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Distill.pub
Open-access journal showcasing machine learning research through interactive, visual articles. Uses dynamic diagrams and animations to explain complex concepts better than static text. Articles cover neural network interpretability, optimization and attention mechanisms. Free to read, with articles by leading researchers. A unique resource for building deep intuition about ML.
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The Batch Newsletter
Free weekly AI newsletter from DeepLearning.AI covering the latest news, breakthroughs and applications of AI. Each issue summarizes key developments in plain language with commentary from Andrew Ng. Includes sections on research, industry, politics and practical learning resources. Trusted by hundreds of thousands of AI professionals and learners.
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Machine Learning Mastery
Blog by Dr Jason Brownlee with hundreds of tutorials on machine learning and deep learning in Python. Focuses on practical step-by-step tutorials with working code using scikit-learn, Keras and PyTorch. Covers data preparation, algorithms, model evaluation and tuning. Free to read, with paid books and courses available for those who want more structure. A favorite practical reference for applied ML.
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Stanford CS229 Machine Learning
Stanford's graduate machine learning course by Andrew Ng, with lecture notes, problem sets and videos freely available. Covers supervised learning, unsupervised learning, learning theory and reinforcement learning. Full course materials are free; attending the Stanford course requires enrollment. The classic rigorous introduction to ML used by universities worldwide. Lecture videos are on YouTube.
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Stanford CS231n Deep Learning for Computer Vision
Stanford course on deep learning for computer vision, with free lecture notes, slides and assignment materials. Covers CNNs, training neural networks, attention, transformers and generative models. Includes detailed notes widely used as a textbook-level reference. Free to access with videos available on YouTube. One of the most influential deep learning courses in the world.
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Neural Networks: Zero to Hero (Karpathy)
Video series by Andrej Karpathy that builds neural networks from scratch in code. Covers backpropagation, micrograd, makemore language models and GPT from first principles. No libraries, just Python, so you see every detail of how networks learn. Free on YouTube and widely praised as the clearest deep-dive into neural network internals.
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TensorFlow Playground
Interactive browser tool for experimenting with neural networks visually. Adjust network depth, learning rate, activation functions and data, and watch training happen in real time. Helps build intuition for overfitting, regularization and hyperparameters. Free with no installation required. Created by Google and a staple first experiment for ML learners.
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Google Colab
Free cloud notebook environment from Google with preinstalled ML libraries and free GPU and TPU access. Write and run Python, PyTorch and TensorFlow code in the browser with zero setup. The free tier includes limited GPU time; paid Pro plans add more compute. Widely used by ML courses, tutorials and Kaggle competitors.
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Hugging Face Hub
Central repository for machine learning models, datasets and demos, hosting hundreds of thousands of open models. Browse, test and deploy models from the browser, or use the Transformers library. The free tier covers public hosting and inference; Pro accounts add private hosting and more features. The standard place where the open-source AI community shares models.
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Dive into Deep Learning (d2l.ai)
Free interactive textbook covering deep learning theory and practice. Code, math and figures in every section, with notebooks for PyTorch, TensorFlow, JAX and MXNet. Covers CNNs, RNNs, attention, transformers, optimization and applications. Runs in Colab and is free online in many languages. Used as a course textbook at top universities.
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arXiv
Free open-access repository hosting over two million research papers in physics, mathematics, computer science, quantitative biology, and more. Read and download full preprints directly, study cutting-edge machine learning research as it appears, and practice by reading primary literature. The standard first stop for studying academic papers.
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Google Research Publications
Official publication hub for Google's research teams, hosting peer-reviewed papers and technical whitepapers on machine learning, systems, and AI. Read primary research documents free, study how production systems are designed, and practice comprehension of dense technical material from one of the leading industrial research labs.
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