Linear Algebra for Data Science with Python

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Linear Algebra for Data Science with Python

Master the mathematical engine driving modern artificial intelligence. Linear Algebra for Data Science with Python provides a rigorous yet practical bridge between abstract mathematical concepts and real-world data science applications. This essential volume demystifies the vectors, matrices, and transformations that form the foundation of machine learning, allowing you to build more accurate and efficient models.

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What You Will Learn:

Foundational Mathematics: Master vectors, matrices, and tensors, and understand how they represent high-dimensional data in modern computing.

Python Implementation: Learn to translate complex linear algebra operations into high-performance code using industry-standard libraries like NumPy and SciPy.

Dimensionality Reduction: Discover the mechanics behind Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) for optimizing large datasets.

Algorithmic Application: Explore how linear transformations and eigenvalues drive the training of neural networks and recommendation systems.

Who This Book is For: This comprehensive resource is essential for aspiring data scientists, machine learning engineers, and software developers who want to move beyond "black-box" libraries. It is a vital reference for any technical professional aiming to master the underlying logic of modern AI and data analysis.

Product Details:

Format: Digital PDF Download

Author: John M. Shea

Publisher: CRC Press LLC

Edition: 1st Edition

Publication Date: October 31, 2025

ISBN-13: 9781032659169

ISBN-10: 1032659165