Twelve parts of linear algebra for programmers who need it for machine learning and never took the course. Vectors, matrices, systems of equations, inverses and determinants, vector spaces and subspaces, eigenvalues and eigenvectors, orthogonality and projections, least squares, singular value decomposition, principal component analysis, and what the whole stack is used for once it is assembled.

The examples are Python with NumPy. The order matters, since each part assumes the one before it, and the later parts are mostly recombinations of the earlier ones. Part one starts with vectors.

For the same mathematics implemented in Go against gonum rather than explained in Python, see Linear Algebra in Go.