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Cambridge University Press Cambridge Mathematics for Machine Learning Textbook

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Description

Master the essential mathematical foundations required for modern data science. This textbook from Cambridge University Press bridges the gap between traditional mathematical theory and practical machine learning applications. It provides a self-contained learning experience designed to help students and professionals move beyond disparate courses and build a cohesive understanding of the field. Instead of studying isolated topics, you will learn how linear algebra, analytic geometry, and matrix decompositions work together with vector calculus and optimization. The text introduces these core concepts with minimal prerequisites, making it accessible for computer science students and industry professionals alike. By focusing on the mathematical tools that drive machine learning, this book helps you understand the logic behind the algorithms. Whether you are pursuing higher education or advancing your career in data science, this book offers a structured path to mastering probability, statistics, and the mathematical derivations used in central machine learning methods. It is an ideal resource for anyone looking to build a solid technical foundation for their machine learning journey.

Key Features

Comprehensive coverage of essential topics including linear algebra, analytic geometry, matrix decompositions, and vector calculus.

Provides a self-contained learning environment that bridges the gap between math theory and machine learning practice.

Designed with minimal prerequisites to make complex mathematical concepts accessible to students and professionals.

Includes vital training in optimization, probability, and statistics to support data science and computer science goals.

High-quality perfect binding format with 390 pages of detailed educational content from Cambridge University Press.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
23 April 2020
Listed Since
26 April 2019

Barcode

No barcode data available

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