Linear algebra provides a mathematical framework for representing and manipulating data, which is essential for learning from data. Many machine learning algorithms, such as linear regression, principal component analysis (PCA), and singular value decomposition (SVD), rely heavily on linear algebra.
For those serious about moving beyond "black box" machine learning, one textbook has emerged as the gold standard: Linear Algebra and Learning from Data by Gilbert Strang (Wellesley-Cambridge Press). But owning the textbook is only half the battle. The real magic happens when you pair the (for reference) with hands-on GitHub repositories (for implementation). linear algebra and learning from data pdf github
This book aims to explain the mathematics on which data science depends: Linear algebra, optimization, probability and statistics. such as linear regression