Mathematics of Machine Learning: Master Linear Algebra, Calculus, and Probability for Machine Learning - Softcover

Tivadar Danka

Softcover

ISBN 10: 1837027870 ISBN 13: 9781837027873

Publisher: Packt Publishing, 2025

Synopsis

Master the math behind machine learning algorithms with this comprehensive guide to linear algebra, calculus, and probability, complete with Python examples

Key Features

• Master linear algebra, calculus, and probability theory for ML
• Understand mathematical structures behind machine learning algorithms
• Learn Python implementations of core mathematical concepts
• Develop skills to optimize, customize, and analyze machine learning solutions
• Bridge the gap between theory and real-world applications

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.

The book balances theory and application, offering clear explanations of mathematical constructs and their direct relevance to machine learning tasks. Through practical Python examples, you’ll not only learn the mathematics but also how to implement and use these ideas in real-world scenarios, such as optimizing algorithms or solving specific challenges in neural network training.

Whether you aim to deepen your theoretical knowledge or enhance your capacity to solve complex machine learning problems, this book provides the structured guidance you need. By the end of this book, you’ll gain the confidence to engage with advanced machine learning literature and tailor algorithms to meet specific project requirements.

What you will learn

• Core concepts of linear algebra, including matrices, eigenvalues, and decompositions
• Fundamental principles of calculus, including differentiation and integration
• Advanced topics in multivariable calculus for optimization in high dimensions
• Essential probability concepts like distributions, Bayes' theorem, and entropy
• Python-based implementations to bring mathematical ideas to life
• The superpower of mathematical and scientific thinking, with applications to data science and machine learning

Who this book is for

This book is for aspiring and practicing machine learning engineers, data scientists, and software developers who wish to gain a deeper understanding of the mathematics that drives machine learning. A foundational understanding of Python and a 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. The structure of real numbers
12. Differentiation
13. Integration
14. Optimization
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

About this title

About the Author

Tivadar Danka is a mathematician by training, a machine learning engineer by profession, and an educator by passion. After finishing his PhD in 2016 (about the arcane subject of orthogonal polynomials), he switched career paths and has been working in machine learning ever since. His work includes applying deep learning to cell microscopy images to identify and phenotype cells, creating one of the most popular open source Python packages for active learning, building a full machine learning library from scratch, and collecting about a total of 100k followers on social media, all by posting high-quality educational content.

Edition details

• Publisher: Packt Publishing
• Publication date: 2025
• ISBN 10: 1837027870
• ISBN 13: 9781837027873
• Binding: Paperback
• Language: English
• Number of pages: 600