---
product_id: 735845880
title: "Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning"
price: "€ 96.77"
currency: EUR
in_stock: true
reviews_count: 5
url: https://www.desertcart.ie/products/735845880-mathematics-of-machine-learning-master-linear-algebra-calculus-and-probability
store_origin: IE
region: Ireland
---

# Probability fundamentals Deep calculus insights Master linear algebra Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning

**Price:** € 96.77
**Availability:** ✅ In Stock

## Summary

> 📚 Decode the math behind AI mastery — don’t get left behind!

## Quick Answers

- **What is this?** Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning
- **How much does it cost?** € 96.77 with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.ie](https://www.desertcart.ie/products/735845880-mathematics-of-machine-learning-master-linear-algebra-calculus-and-probability)

## Best For

- Customers looking for quality international products

## Why This Product

- Free international shipping included
- Worldwide delivery with tracking
- 15-day hassle-free returns

## Key Features

- • **Highly Rated Resource:** Trusted by learners with a solid 4.3-star average
- • **Paperback Convenience:** Portable and easy to reference anytime, anywhere
- • **Bridge Theory & Practice:** Connect complex math directly to machine learning code
- • **Future-Proof Your Skills:** Stay ahead with 2025’s latest mathematical frameworks
- • **Comprehensive Core Topics:** Covers linear algebra, calculus, and probability essentials

## Overview

This 2025 paperback from Packt Publishing offers a rigorous dive into the mathematics powering machine learning, focusing on linear algebra, calculus, and probability. Ideal for professionals aiming to deepen their theoretical foundation and practical understanding, it bridges complex concepts with programming applications. With a strong 4.3-star rating and recognized rankings in programming and math categories, it’s a must-have for ambitious learners ready to elevate their AI expertise.

## 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 - Vectors and vector spaces - The geometric structure of vector spaces - Linear algebra in practice spaces: measuring distances - Linear transformations - Matrices and equations - Eigenvalues and eigenvectors - Matrix factorizations - Matrices and graphs - Functions - Numbers, sequences, and series - Topology, limits, and continuity - Differentiation - Optimization - Integration - Multivariable functions - Derivatives and gradients - Optimization in multiple variables - What is probability? - Random variables and distributions - The expected value - The maximum likelihood estimation - It's just logic - The structure of mathematics - Basics of set theory - Complex numbers

Review: Too complex, at points, for its intended audience. - As others have noted in different words, the issue with this book is that it seems to assume a mathematical proficiency greater than those of its intended readers, a common failing with expert authors, who take some of their knowledge for granted. Programmers or Data Scientists who haven't done a Mathematics degree might be able to get through this book, but they will need to refer externally to make sense of the book at several (too many) points. The exercises too, IMO, are more for the maths aficionado than someone who just wants a very practical applied exposition and understanding. If, however, you have a strong undergraduate math background, you may like this book over simpler ones.
Review: remarquable et original - livre remarquablement expliqué; faisant le lien entre les mathématiques et la programmation

## Features

- Binding: paperback
- Language: english
- Manufacturer: Packt Publishing
- Publication date: 2025-05-30T00:00:00.000Z

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #86,772 in Books ( See Top 100 in Books ) #46 in Programming Algorithms #49 in Mathematical Analysis #6,340 in Education Studies & Teaching |
| Customer Reviews | 4.3 out of 5 stars 69 Reviews |

## Images

![Mathematics of Machine Learning: Master linear algebra, calculus, and probability for machine learning - Image 1](https://m.media-amazon.com/images/I/71W4Rs1w2IL.jpg)

## Customer Reviews

### ⭐⭐⭐ Too complex, at points, for its intended audience.
*by D***T on 2 February 2026*

As others have noted in different words, the issue with this book is that it seems to assume a mathematical proficiency greater than those of its intended readers, a common failing with expert authors, who take some of their knowledge for granted. Programmers or Data Scientists who haven't done a Mathematics degree might be able to get through this book, but they will need to refer externally to make sense of the book at several (too many) points. The exercises too, IMO, are more for the maths aficionado than someone who just wants a very practical applied exposition and understanding. If, however, you have a strong undergraduate math background, you may like this book over simpler ones.

### ⭐⭐⭐⭐⭐ remarquable et original
*by G***E on 12 July 2025*

livre remarquablement expliqué; faisant le lien entre les mathématiques et la programmation

### ⭐⭐⭐ Needs full colour or edited charts
*by S***S on 28 July 2025*

The content must be great - can’t comment since I’m just on Chap.1 - but the author and publisher should both either prepare to print in full colour or ensure charts are not colour-dependent. Right now there are density plots in all grey, which according to the code should have been red or blue or green. Do better, please. Also, for the softcover copy, the book’s size needs thicker binding to make it longer-lasting.

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*Product available on Desertcart Ireland*
*Store origin: IE*
*Last updated: 2026-06-09*