---
product_id: 156688087
title: "Mathematics for Machine Learning"
price: "530 Lei"
currency: RON
in_stock: true
reviews_count: 8
url: https://www.desertcart.ro/products/156688087-mathematics-for-machine-learning
store_origin: RO
region: Romania
---

# Mathematics for Machine Learning

**Price:** 530 Lei
**Availability:** ✅ In Stock

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- **What is this?** Mathematics for Machine Learning
- **How much does it cost?** 530 Lei with free shipping
- **Is it available?** Yes, in stock and ready to ship
- **Where can I buy it?** [www.desertcart.ro](https://www.desertcart.ro/products/156688087-mathematics-for-machine-learning)

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## Description

Mathematics for Machine Learning [Deisenroth, Marc Peter] on desertcart.com. *FREE* shipping on qualifying offers. Mathematics for Machine Learning

Review: Wonderfully illustrated, welll laid out, great website and extra content - If you already have a background with linear algebra, calculus, statistics, then this will be a nice refresher applied to the subject in question, machine learning. In that regard, it serves perfectly as a way to organize your study to get into AI/ML in a deeper way. Certainly deeper than from a purely user perspective. If you don't have a background with linear algebra, calculus, statistics, it'll still provide a well organized studies plan for you to dive deeper. It cannot, of course, be a textbook for these areas, it would take hundreds, thousands of pages to do so, and that's clearly not feasible. What it does is introduce you to some concepts, refresh them, or refer you to further studies where there is a need to dive deeper in certain topics. The book is clearly organized, well illustrated. For that alone I'm thankful, for many mathematics textbooks, even the ones targeting the professional mathematician, make the fatal mistake of assuming the reader finds images insulting. They're not. Images help you organize thoughts visually, geometrically, providing important insights. For that alone, the content and organization, I would give the book 5 stars. The examples are well laid out, the cases well illustrated, giving room for the reader to breathe without being crushed by a dense monolith of rendered equations. Where it exceeds and stands above others is that the companion website provides, freely, the PDF of the book, an errata, instructor solutions to the exercises, and Jupyter Lab notebooks. While other publishers would try to rob the customer blind by offering each of these as a separate product, for a hefty sum naturally, this publisher thought it would best serve the reader to have access to all this content for free. Naturally, in this day and era, seeing someone focused on spreading knowledge for the sake of science and knowledge is commendable, and I cannot give me more than 5 stars sadly, for I would. If you read it this far, this is a no-brainer. Visit the website, take a look at the PDF, buy it, so that you can have the version with you for your daily studies, and the PDF for that morning reading on the tablet. The Jupyter notebooks make exploration fun and interesting, even if you're not experienced in the field. It does not assume you are an expert in these areas, though naturally, it would benefit you greatly if you have experience or if at least you have some textbooks on linear algebra and some knowledge of differential, integral calculus. To the authors, congratulations, and to the publisher, may you have a thousand years of prosperity and good fortune for making the auxiliary content freely available and in such a open and honest manner. Bravo. Highly recommended.
Review: Incredible Resource - I had been looking for a book to bridge the gap between implementing machine learning code on the granular level and understanding it from a theoretical perspective and the search wasn't going well. Lots of other books that I tried before finding this one promised to help programmers become better mathematicians (or at least show them the math they need to learn in order to achieve that goal) but would almost always just provide code without context, or run through some incredibly basic, introductory level math without explaining at all how it connects to the various machine learning algorithms you'll be implementing as a programmer. This book, however, takes the math seriously, and is incredibly direct and efficient in the introduction of new, relevant topics in calculus, linear algebra, and probability and statistics that you'll need to know if you want to truly understand the libraries you're using. I find myself reading a section in the book, going back to a "dedicated" textbook on the subject at hand - linear algebra or calculus or probability and statistics - and further studying the material, and then going back to Mathematics for Machine Learning to make sure I understand the topic better. This is the exact learning flow that I wanted, and the book delivers. Can't recommend enough!

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #32,103 in Books ( See Top 100 in Books ) #7 in Computer Vision & Pattern Recognition #29 in Computer Science (Books) |
| Customer Reviews | 4.6 out of 5 stars 993 Reviews |

## Images

![Mathematics for Machine Learning - Image 1](https://m.media-amazon.com/images/I/71ZPgUTDn6L.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ Wonderfully illustrated, welll laid out, great website and extra content
*by L***S on January 20, 2025*

If you already have a background with linear algebra, calculus, statistics, then this will be a nice refresher applied to the subject in question, machine learning. In that regard, it serves perfectly as a way to organize your study to get into AI/ML in a deeper way. Certainly deeper than from a purely user perspective. If you don't have a background with linear algebra, calculus, statistics, it'll still provide a well organized studies plan for you to dive deeper. It cannot, of course, be a textbook for these areas, it would take hundreds, thousands of pages to do so, and that's clearly not feasible. What it does is introduce you to some concepts, refresh them, or refer you to further studies where there is a need to dive deeper in certain topics. The book is clearly organized, well illustrated. For that alone I'm thankful, for many mathematics textbooks, even the ones targeting the professional mathematician, make the fatal mistake of assuming the reader finds images insulting. They're not. Images help you organize thoughts visually, geometrically, providing important insights. For that alone, the content and organization, I would give the book 5 stars. The examples are well laid out, the cases well illustrated, giving room for the reader to breathe without being crushed by a dense monolith of rendered equations. Where it exceeds and stands above others is that the companion website provides, freely, the PDF of the book, an errata, instructor solutions to the exercises, and Jupyter Lab notebooks. While other publishers would try to rob the customer blind by offering each of these as a separate product, for a hefty sum naturally, this publisher thought it would best serve the reader to have access to all this content for free. Naturally, in this day and era, seeing someone focused on spreading knowledge for the sake of science and knowledge is commendable, and I cannot give me more than 5 stars sadly, for I would. If you read it this far, this is a no-brainer. Visit the website, take a look at the PDF, buy it, so that you can have the version with you for your daily studies, and the PDF for that morning reading on the tablet. The Jupyter notebooks make exploration fun and interesting, even if you're not experienced in the field. It does not assume you are an expert in these areas, though naturally, it would benefit you greatly if you have experience or if at least you have some textbooks on linear algebra and some knowledge of differential, integral calculus. To the authors, congratulations, and to the publisher, may you have a thousand years of prosperity and good fortune for making the auxiliary content freely available and in such a open and honest manner. Bravo. Highly recommended.

### ⭐⭐⭐⭐⭐ Incredible Resource
*by A***R on September 3, 2022*

I had been looking for a book to bridge the gap between implementing machine learning code on the granular level and understanding it from a theoretical perspective and the search wasn't going well. Lots of other books that I tried before finding this one promised to help programmers become better mathematicians (or at least show them the math they need to learn in order to achieve that goal) but would almost always just provide code without context, or run through some incredibly basic, introductory level math without explaining at all how it connects to the various machine learning algorithms you'll be implementing as a programmer. This book, however, takes the math seriously, and is incredibly direct and efficient in the introduction of new, relevant topics in calculus, linear algebra, and probability and statistics that you'll need to know if you want to truly understand the libraries you're using. I find myself reading a section in the book, going back to a "dedicated" textbook on the subject at hand - linear algebra or calculus or probability and statistics - and further studying the material, and then going back to Mathematics for Machine Learning to make sure I understand the topic better. This is the exact learning flow that I wanted, and the book delivers. Can't recommend enough!

### ⭐⭐⭐⭐⭐ Brilliant and Precise
*by E***C on April 29, 2020*

The book is the missing piece between books like Artificial Intelligence: A Modern Approach and the mathematics you require to take such an undertaking. The authors do assume very little prior knowledge from the reader, but it t is recommended that you've had exposure to some of the mathematical topics prior to reading the book. But don't let that stop you if you're a beginner: you'll have to make a few detours to grasp some terms and such. Having said that, a course on single variable calculus ought to be under your belt. That's basically the only prerequisite. The explanations are clear, and the book is designed to bring clarity and lucidity onto the topics, not send the student on an endless pit of proofs and rigor.

## Frequently Bought Together

- Mathematics for Machine Learning
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Deep Learning (Adaptive Computation and Machine Learning series)

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*Product available on Desertcart Romania*
*Store origin: RO*
*Last updated: 2026-02-26*