Stanford Online / DeepLearning.AI · on Coursera
Andrew Ng's updated three-course sequence covers regression, classification, neural networks, decision trees, clustering, and recommender systems in Python. Take it as the canonical first ML course if you want a rigorous but gentle on-ramp with hands-on labs.
Aspiring data scientists and ML engineers. This is a beginner-level course in Machine Learning, running about 2 months at 10 hrs/week (~95 hours, 3 courses).
Free to audit · $49/mo Coursera subscription for graded work and certificate. Financial aid available. Price seen on Coursera FAQ 2026-11-01; certificates require payment.
A micro-course that gets you from zero to your first random forest model and Kaggle competition submission in an afternoon. Perfect for people who learn by doing and want immediate, runnable code.
Google's refreshed crash course mixes short videos, interactive visualizations, and quizzes across linear and logistic regression, data handling, neural networks, embeddings, LLMs, and production ML. Ideal for engineers who want a practical, fast overview before committing to a longer program.
MIT's undergraduate ML course (Fall 2020) with notes, auto-graded exercises, and labs covering perceptrons, gradient descent, neural nets, CNNs, RNNs, and reinforcement learning. Best for learners comfortable with linear algebra who want an MIT-level problem-set experience for free.