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Mathematics of Machine Learning Cover

Mathematics of Machine Learning

Master linear algebra, calculus, and probability for machine learning

Publisher:Packt Publishing Limited
By:  and    
Paid access
|May 2025
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Description

Build a solid foundation in the core math behind machine learning algorithms with this comprehensive guide to linear algebra, calculus, and probability, explained through practical Python examples

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

  • Master linear algebra, calculus, and probability theory for ML
  • Bridge the gap between theory and real-world applications
  • Learn Python implementations of core mathematical concepts

Book Description

Mathematics of Machine Learning provides a rigorous yet accessible introduction to the mathematical underpinnings of machine learning, designed for engineers, developers, and data scientists ready to elevate their technical expertise. With this book, you’ll explore the core disciplines of linear algebra, calculus, and probability theory essential for mastering advanced machine learning concepts.

PhD mathematician turned ML engineer Tivadar Danka—known for his intuitive teaching style that has attracted 100k+ followers—guides you through complex concepts with clarity, providing the structured guidance you need to deepen your theoretical knowledge and enhance your ability to solve complex machine learning problems. Balancing theory with application, this book offers clear explanations of mathematical constructs and their direct relevance to machine learning tasks. Through practical Python examples, you’ll learn to implement and use these ideas in real-world scenarios, such as training machine learning models with gradient descent or working with vectors, matrices, and tensors.

By the end of this book, you’ll have gained the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements.

What you will learn

  • Understand core concepts of linear algebra, including matrices, eigenvalues, and decompositions
  • Grasp fundamental principles of calculus, including differentiation and integration
  • Explore advanced topics in multivariable calculus for optimization in high dimensions
  • Master essential probability concepts like distributions, Bayes' theorem, and entropy
  • Bring mathematical ideas to life through Python-based implementations

Who this book is for

This book is for aspiring machine learning engineers, data scientists, software developers, and researchers who want to gain a deeper understanding of the mathematics that drives machine learning. A foundational understanding of algebra and Python, and basic familiarity with machine learning tools are recommended.

Table of Contents

  1. Vectors and vector spaces
  2. The geometric structure of vector spaces
  3. Linear algebra in practice spaces: measuring distances
  4. Linear transformations
  5. Matrices and equations
  6. Eigenvalues and eigenvectors
  7. Matrix factorizations
  8. Matrices and graphs
  9. Functions
  10. Numbers, sequences, and series
  11. Topology, limits, and continuity
  12. Differentiation
  13. Optimization
  14. Integration
  15. Multivariable functions
  16. Derivatives and gradients
  17. Optimization in multiple variables
  18. What is probability?
  19. Random variables and distributions
  20. The expected value
  21. The maximum likelihood estimation
  22. It's just logic
  23. The structure of mathematics
  24. Basics of set theory
  25. Complex numbers
https://github.com/cosmic-cortex/mathematics-of-machine-learning-book/
PDF ISBN: 978-1-83702-786-6
Publisher: Packt Publishing Limited
Copyright owner: © 2025 Packt Publishing
Publication date: 2025
Language: English
Pages: 730
Related subjects:
Computer sciences,
Algorithms
BISAC subjects:
COM051300,
MAT034000,
MAT041000
Thema subjects:
UMB,
PBK,
PBKS

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