Before Machine Learning Volume 1 - Linear Algebra for A.I

You keep hearing that linear algebra is essential for machine learning. You've tried the textbooks, maybe even a few YouTube playlists, but everything either assumes you already know it or drowns you in abstract proofs with no connection to anything real. This book takes a different route. Using a movie recommendation system as a running example, this book builds your linear algebra intuition from the ground up. You start with a simple two dimensional vector, just ratings and budget, and by the end you're performing Principal Component Analysis. Every concept earns its place by showing you exactly how and why it matters in machine learning. You'll learn vectors, vector addition, scalar multiplication, the dot product, cosine similarity, vector spaces, matrices, linear transformations, determinants, matrix inverses, systems of linear equations, eigenvalues and eigenvectors, matrix diagonalization, matrix decomposition, Singular Value Decomposition (SVD), and PCA, all without assuming anything beyond basic arithmetic. The tone is conversational, sometimes funny, and always honest about what's hard. Equations are not skipped. They're explained, motivated, and connected to something you can actually picture.