Linear Algebra for Data Science: Matrix Methods for Machine Learning
English | May 7, 2026 | ASIN: B0GZSZ63BV | 347 pages | Epub | 0.4 MB
Reactive PublishingLinear algebra is one of the core mathematical foundations behind modern data science, machine learning, and computational modeling. Linear Algebra for Data Science: Matrix Methods for Machine Learning provides a practical and structured introduction to the concepts that help readers understand how data is represented, transformed, compressed, and analyzed through vectors, matrices, and linear systems.This book is designed for readers who want to move beyond surface-level machine learning workflows and develop a stronger mathematical understanding of the models, algorithms, and transformations used in data science. It explains the essential ideas of linear algebra in a clear, applied way, connecting each concept to real computational use cases.Inside, readers will explore:Vectors, vector spaces, norms, and geometric interpretationMatrices, matrix operations, and linear transformationsSystems of linear equations and their role in modelingEigenvalues, eigenvectors, and dimensional structureMatrix factorization methods used in machine learningOrthogonality, projections, least squares, and regression foundationsPrincipal component analysis and dimensionality reductionHow linear algebra supports optimization, embeddings, recommendation systems, and model architectureRather than treating linear algebra as abstract theory alone, this book emphasizes how mathematical structure becomes practical computational power. Readers will see how matrix methods support data preprocessing, feature engineering, model training, numerical stability, and high-dimensional analysis.Whether you are a data science student, machine learning practitioner, analyst, developer, or self-directed learner, this book offers a clear pathway into the mathematics that underpins modern AI and data-driven systems.Linear Algebra for Data Science gives readers the conceptual foundation needed to understand machine learning models more deeply, reason about data more effectively, and build stronger technical intuition.
Links are Interchangeable - Single Extraction
