LINEAR ALGEBRA for MACHINE LEARNING A Visual, Step-by-Step Guide with Python to Master Vectors, Matrices, PCA, SVD

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LINEAR ALGEBRA for MACHINE LEARNING : A Visual, Step-by-Step Guide with Python to Master Vectors, Matrices, PCA, SVD, and Neural Networks
English | May 9, 2026 | ASIN: B0H15P5Q89 | 370 pages | Epub | 5.88 MB
Master the math behind modern AI without getting lost in theory.Whether you want to understand neural networks, build machine learning models from scratch, or finally make sense of matrices and vectors, this book gives you a practical, visual, and beginner-friendly path into the linear algebra that powers artificial intelligence.Linear Algebra for Machine Learning transforms difficult mathematical ideas into clear, intuitive concepts with real-world ML applications, visual explanations, and hands-on Python examples.Inside this book, you'll learn how linear algebra drives:Machine learning algorithmsNeural networks and deep learningRecommendation systemsPCA and dimensionality reductionImage compression and embeddingsOptimization and backpropagationSearch engines and vector databasesThis book is designed specifically for:Machine learning beginnersData science studentsSelf-taught AI learnersComputer science studentsPython programmers entering AIAnyone who wants to truly understand ML mathWhat makes this book different:Step-by-step explanations with intuition firstMinimal prerequisites - only basic algebra requiredVisual approach to vectors, matrices, and transformationsPython + NumPy examples throughoutReal ML applications in every sectionPractical projects and worked examplesClear explanations of PCA, SVD, neural networks, and optimizationInside, you'll discover:Vector operations and geometric intuitionMatrix multiplication and transformationsLinear regression from scratchOrthogonality and projectionsEigenvalues and eigenvectorsPrincipal Component Analysis (PCA)Singular Value Decomposition (SVD)Neural network math simplifiedFeature engineering and embeddingsOptimization fundamentalsReal-world machine learning projectsYou'll also build:A recommendation systemAn image compressor using SVDA mini neural networkA PCA visualization projectA document search engineAnd moreBy the end of this book, you won't just use machine learning libraries - you'll understand the mathematics behind them.If you're ready to finally connect linear algebra with real AI systems, this book will give you the foundation you nee​

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