---
product_id: 2214053
title: "Machine Learning: The Art and Science of Algorithms that Make Sense of Data"
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# Machine Learning: The Art and Science of Algorithms that Make Sense of Data

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## Description

Machine Learning: The Art and Science of Algorithms that Make Sense of Data [Flach, Peter] on desertcart.com. *FREE* shipping on qualifying offers. Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Review: Approachable, dense, and beautiful book on a wonderful subject - What an amazing book, I got it about a month ago for a self-study routine and every page of this book has been a joy. I am an undergraduate CS major with a decent amount of math experience, and for me this book is a tough but rewarding read. I constantly find myself reading the same section 2 or 3 times in a row, restling with the concepts until I can grasp some intuition of the topics bring discussed. The author is very thorough in their writing, making sure to fill in the details so you dont get left behind in the mathematical notation. The book is filled with beautiful graphs and other figures to further help the reader along in their understanding of machine learning. As a heads up, this book is heavy on the theory and light on the application, so keep that in mind when considering this book for purchase. It isn't going to give you a simple recipe to plug into R. It did however, lay out the intricacies of machine learning in a very abstract and methodical fashion, allowing the reader to gain a much deeper insight into the mechanics of the popular ML techniques than a more practical book would.
Review: ML is hard topic. - At first. I am rewriting review for this book. This books covers fundamental theory about ML. So I was very frustracted because lack of mathmetical background. But once you get use to it. This book will be definite guide book for ML study. Admitting..this book is hard to read. but worth it. and can't be easier because ML itself is VERY hard topic !!!

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #1,848,476 in Books ( See Top 100 in Books ) #204 in Computer Vision & Pattern Recognition #8,596 in Computer Science (Books) |
| Customer Reviews | 4.2 4.2 out of 5 stars (88) |
| Dimensions  | 7.44 x 0.98 x 9.69 inches |
| Edition  | 1st |
| ISBN-10  | 1107422221 |
| ISBN-13  | 978-1107422223 |
| Item Weight  | 1.95 pounds |
| Language  | English |
| Print length  | 409 pages |
| Publication date  | November 12, 2012 |
| Publisher  | Cambridge University Press |

## Images

![Machine Learning: The Art and Science of Algorithms that Make Sense of Data - Image 1](https://m.media-amazon.com/images/I/71hXFJuIHvL.jpg)
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## Customer Reviews

### ⭐⭐⭐⭐⭐ Approachable, dense, and beautiful book on a wonderful subject
*by J***N on December 29, 2014*

What an amazing book, I got it about a month ago for a self-study routine and every page of this book has been a joy. I am an undergraduate CS major with a decent amount of math experience, and for me this book is a tough but rewarding read. I constantly find myself reading the same section 2 or 3 times in a row, restling with the concepts until I can grasp some intuition of the topics bring discussed. The author is very thorough in their writing, making sure to fill in the details so you dont get left behind in the mathematical notation. The book is filled with beautiful graphs and other figures to further help the reader along in their understanding of machine learning. As a heads up, this book is heavy on the theory and light on the application, so keep that in mind when considering this book for purchase. It isn't going to give you a simple recipe to plug into R. It did however, lay out the intricacies of machine learning in a very abstract and methodical fashion, allowing the reader to gain a much deeper insight into the mechanics of the popular ML techniques than a more practical book would.

### ⭐⭐⭐⭐⭐ ML is hard topic.
*by M***G on July 25, 2016*

At first. I am rewriting review for this book. This books covers fundamental theory about ML. So I was very frustracted because lack of mathmetical background. But once you get use to it. This book will be definite guide book for ML study. Admitting..this book is hard to read. but worth it. and can't be easier because ML itself is VERY hard topic !!!

### ⭐⭐⭐⭐ Great second book on Machine Learning!
*by N***A on November 2, 2016*

In real world, three cohorts would approach Machine Learning differently - A. Programmers - "How" - interested in quickly learning the libraries, tips/tricks to scale algorithms with larger data sets B. Theorists - "What" - interested in choosing the right algorithm, design ensemble, selecting and extracting right features C. Fashionists - "Show" - in this category, some of the even basic reporting/analytics are not termed "Machine Learning", need enough buzzwords pieced together to repaint the old apps. Flach's book is a great source for those who are 75%-25% between first two, and perhaps even greater especially if your Linear Algebra (basics) is not too rusty. It gives a wide and somewhat deep tour of the landscape broken into four paradigms (Quantitative/Analytical, Logical, Geometric, Probabilitisic) and does a real good job on feature design. The book is interspersed with some key insights that are not to be found elsewhere (e.g., how the 'pseudo-inverse' in OLS is really decorrelate-scale-normalize the distribution; Skew-Kurtosis are the statistical measure of "shape"; Naive Bayes is not only Naive but also not particularly Bayesian; How Laplacian Estimate generalizes into Pseudo-Counts and then to m-estimate etc.). After "deep reading" of the book over a month or so, I also went through Flach's detailed 500+ slide presentation (check out his website) on this book. It was very useful to improve solutions several key machine learning problems at work. Flach especially shines on usage of ROC to algorithm comparison which has been his key research area. A few items that I think would've nailed 5-stars - 1. Total omission of Neural Nets (ANNs) 2. Only a glimpse of RBF while discussing the generalization from kNN to GMMs - as a key activation function more detailed treatment on RBF would help. 3. Flach does a really good job of summarizing - at the end of each chapter and at the end of the book - the key insights. A similar "Real World Insights", which are interspersed in the book (e.g., how Naive Bayes is a GREAT classifier, but lousy estimator), aggregated would have helped. Overall, going back in time, I would buy and study it again. For a great first book, I recommend Hastie's "An Introduction to Statistical Learning", or Hal Duame's "A Course In Machine Learning" (ciml.info). After finishing this book, I would recommend "Pattern Classification" (Duda, Stork) which further elaborates on most stuff here and also has a great elucidation on Neural Networks.

## Frequently Bought Together

- Machine Learning: The Art and Science of Algorithms that Make Sense of Data
- The Hundred-Page Machine Learning Book (The Hundred-Page Books)

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*Last updated: 2026-03-17*