Mathematics of Machine Learning

Name: Mathematics of Machine Learning
Brand: Packt Publishing
Price: 44.99 AUD
Availability: PreOrder

Master Linear Algebra, Calculus, and Probability for Machine Learning

By: Tivadar Danka

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eBook | 30 May 2025

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Available: 30th May 2025

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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.