Mathematics for Machine Learning: An Overview

OVERFIT: AI, Machine Learning, and Deep Learning Made Simple

The book titled "Mathematics for Machine Learning" explains various mathematical concepts that are essential for understanding machine learning algorithms, including linear algebra, analytic geometry, vector calculus, and probability. It also discusses topics such as model selection, parameter estimation, dimensionality reduction, and classification, providing a comprehensive overview of the mathematical foundations of machine learning. The text delves into concepts like eigenvalues, eigenvectors, matrix decompositions, and gradients, demonstrating their significance in different machine learning methods.

You can get the book PDF for free here: https://mml-book.github.io/