Foundational algorithms, techniques, and theory for building intelligent systems that learn from data.
- Coursera: Machine Learning by Andrew Ng - The gold standard introductory ML course covering supervised/unsupervised learning, best practices.
Beginner - Google: Machine Learning Crash Course - Fast-paced, interactive course with TensorFlow exercises and real-world case studies.
Beginner - fast.ai: Practical Deep Learning for Coders - Top-down, hands-on approach to learning ML and deep learning.
Beginner - Kaggle: Intro to Machine Learning - Short, practical course with real datasets and in-browser coding.
Beginner - Stanford CS229: Machine Learning - Andrew Ng's full Stanford course with lecture notes, problem sets, and exams.
Intermediate - Coursera: Applied Machine Learning in Python - Hands-on ML with scikit-learn covering classification, regression, and evaluation.
Intermediate - Caltech: Learning from Data - Rigorous ML theory course with video lectures and homework.
Intermediate - Cornell CS4780: Machine Learning for Intelligent Systems - Free lecture videos and notes on ML fundamentals.
Intermediate
- An Introduction to Statistical Learning (ISLR) - Classic free textbook with R/Python labs covering regression, classification, resampling, and more.
Beginner - Hands-On Machine Learning (GitHub Notebooks) - Free Jupyter notebooks companion to the popular Scikit-Learn and TensorFlow book.
Intermediate - Pattern Recognition and Machine Learning (Bishop) - Classic ML textbook now freely available from Microsoft Research.
Advanced
- Scikit-learn Documentation & Tutorials - Official tutorials for the most popular Python ML library.
Beginner - Kaggle Competitions - Real-world ML competitions to practice and build your portfolio.
All Levels - MLflow Documentation - Free open-source platform for managing the ML lifecycle.
Intermediate