Linear Algebra for Data Science and AI Book 1. Vectors, Matrices, and Linear Transformations

Linear Algebra for Data Science and AI: Book 1. Vectors, Matrices, and Linear Transformations (Linear Algebra for Data Science & AI)
English | January 14, 2026 | ASIN: B0GGT2Q4VS | 266 pages | Epub | 9.73 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 1 builds the foundation of linear algebra for readers in data science and AI. You will learn vectors, norms, dot/inner products, vector spaces, bases, and dimension-then connect these ideas to matrices, rank, and linear transformations. The emphasis is conceptual: each definition is explained in plain language, supported by geometric intuition, and tied to practical meaning in machine learning and data analytics. This series focuses on understanding rather than calculation drills and does not include coding or implementation.