The math behind machine learning: the vector/matrix data model, matrix calculus and Jacobians, backpropagation as the chain rule, gradient descent and its convergence, stochastic gradient descent, loss functions (MSE, cross-entropy, softmax), regularization, PCA via SVD, the kernel trick, the bias–variance tradeoff, and the probabilistic (maximum-likelihood) view of regression.
Free to start · adaptive placement finds your level · reviews timed to your own forgetting.
Every idea is taught with motivation and a worked example before the drills, and an FSRS spaced-repetition engine schedules each review for the day its model predicts you would forget it. A short placement check finds what you already know, so you start Mathematics of Machine Learning exactly where it's useful.