This repository contains my excercise notebooks of Mathmatics for machine learning. The first part covers what linear algebra is and how it relates to data. Then it look through what vectors and matrices are and how to work with them.The second part, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The third part, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two parts to compress high-dimensional data.