Linear Algebra for Data Science and AI Book 2. Systems, Orthogonality, and Least Squares

Linear Algebra for Data Science and AI: Book 2. Systems, Orthogonality, and Least Squares (Linear Algebra for Data Science & AI)
English | January 14, 2026 | ASIN: B0DZXXZ3TB | 149 pages | Epub | 4.73 MB
Linear Algebra for Data Science and AI is a five-book textbook series designed to take readers from a first encounter with linear algebra to a level of conceptual mastery that supports advanced study and research in data science and AI. The series emphasizes clear, intuitive explanation in words-supported by rigorous definitions and carefully chosen formulas-so that readers can understand not only how linear algebra is written, but what it means.This series is theory- and concept-driven. It does not include coding, AI implementation, or drill-style calculation practice. Instead, each topic is motivated by how it appears in machine learning, deep learning, and modern data analytics, helping readers connect abstract ideas to practical meaning while building a durable foundation across the five volumes.Book 2 turns linear algebra into a geometric toolkit for estimation. Building on vectors, spaces, and linear transformations, you will study systems of linear equations, Gaussian elimination, and LU decomposition-then develop orthogonality, projections, and the logic of least squares. The goal is to make regression and approximation conceptually transparent: least squares is presented as orthogonal projection onto a subspace, not as a formula to memorize. The series emphasizes intuitive explanation and theoretical understanding, without calculation drills or coding.