Three visual, interactive guides to the maths behind machine learning. Each one starts from zero, explains things in plain English with worked examples, and has interactive visuals you can play with. Your chapter progress is saved in your browser.

Read them in this order, since each one builds on the one before:

1. Linear Algebra for Machine Learning: vectors, matrices and the tools that models are built from.
2. Calculus for Machine Learning: derivatives, gradients and how a model learns from its errors.
3. Optimization for Machine Learning: gradient descent and its relatives, which put the first two to work.