Linear Algebra for Machine Learning

Vectors and Matrices Fundamentals

Learn the essential building blocks of linear algebra: vectors and matrices, and how to represent them computationally.

Matrix Decomposition and Eigenvalues

Master matrix decomposition techniques and eigenvalues that are critical for dimensionality reduction in machine learning.

Vector Spaces and Linear Transformations

Understand vector spaces, linear transformations, and their geometric interpretations essential for ML algorithms.

Norms, Distances, and Orthogonality

Learn distance metrics and orthogonal concepts crucial for similarity measures and regularization in machine learning.

Linear Systems and Applications to Machine Learning

Solve linear systems and apply linear algebra techniques to real machine learning problems like least squares regression.