Caltech
Yaser Abu-Mostafa's celebrated course explains why learning works: VC dimension, bias-variance, overfitting, regularization, and validation, alongside core algorithms. Take it if you want the theoretical intuition that most applied courses skip.
Aspiring data scientists and ML engineers. This is an intermediate course in Machine Learning, running about 18 lectures plus homework.
Free. No payment required at any point.
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.
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.