Mathematics for Machine Learning
A clear introduction to the essential mathematics behind machine learning, covering linear algebra, calculus, probability, and optimization.
About This Book
This book provides a structured introduction to the mathematical principles that underpin modern machine learning.
Readers will explore linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics.
Each topic is presented with clear explanations and practical relevance to machine learning applications.
The material is designed to build a solid mathematical foundation for students and practitioners entering the field.
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