Machine Learning for Hackers: Case Studies and Algorithms to Get You Started 1st Edition

by Drew Conway (Author), John Myles White (Author)

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ISBN-13: 978-1449303716

ISBN-10: 1449303714

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If you’re an experienced programmer interested in crunching data, this book will get you started with machine learning—a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.

Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you’ll learn how to analyze sample datasets and write simple machine learning algorithms. Machine Learning for Hackers is ideal for programmers from any background, including business, government, and academic research.

Develop a naïve Bayesian classifier to determine if an email is spam, based only on its text
Use linear regression to predict the number of page views for the top 1,000 websites
Learn optimization techniques by attempting to break a simple letter cipher
Compare and contrast U.S. Senators statistically, based on their voting records
Build a “whom to follow” recommendation system from Twitter data

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Editorial Reviews

About the Author

Drew Conway is a PhD candidate in Politics at NYU. He studies international relations, conflict, and terrorism using the tools of mathematics, statistics, and computer science in an attempt to gain a deeper understanding of these phenomena. His academic curiosity is informed by his years as an analyst in the U.S. intelligence and defense communities.

John Myles White is a PhD candidate in Psychology at Princeton. He studies pattern recognition, decision-making, and economic behavior using behavioral methods and fMRI. He is particularly interested in anomalies of value assessment.

Product details

Publisher : O'Reilly; 1st edition (March 6, 2012)
Language: : English
Paperback : 324 pages
ISBN-10 : 1449303714
ISBN-13 : 978-1449303716
Item Weight : 1.14 pounds
Dimensions : 7 x 0.75 x 9.19 inches

Best Sellers Rank: #1,232,653 in Books (See Top 100 in Books)
- #207 in Natural Language Processing (Books)
- #269 in Computer Algorithms
- #342 in Machine Theory (Books)

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Top reviews from the United States

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Graham

Entry Level R Programming Book

Reviewed in the United States on November 13, 2016

Verified Purchase

This text offers a detailed description in each of 10 case studies about how to build a machine learning solution to the particular problem mentioned. The authors do this in R, and are extensively descriptive about the mechanics of writing R code. If you've never written a computer program, but want to understand how to implement a prewritten machine learning tool in R, this book could be of assistance. However, I'm not sure the authors actually understand the mathematical theory of machine learning; in this book they constantly substitute descriptions of how to select appropriate algorithm parameters with a trial-and-error approach, they do not explain how the algorithms work, and I'm not sure they ever mention the mechanics of "learning" with respect to mathematics. The book has brief passages about machine learning hidden amongst vast chapters about how to read computer directories and load data, etc. Again this book could be useful to the reader who understands ML, but not computer programming.

3 people found this helpful

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P. Kim

R book with little machine learning

Reviewed in the United States on May 25, 2017

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Not much machine learning but more R.

Not really for hackers but those who want to learn and use R better.

I liked it but it did not help me much with machine learning.

3 people found this helpful

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D Marx

You can do better

Reviewed in the United States on February 24, 2016

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This book is just ok and barely touches the surface of the topics it discusses. If you're looking for an introduction to machine learning and the R language, I think you're better off with "Data Mining with R" by Torgo. It's a bit more expensive, but not without good reason.

2 people found this helpful

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anthony

Ok introduction to R but with bad code

Reviewed in the United States on June 2, 2013

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This book is more of an introduction to R then anything to do with Machine Learning.. as a R introduction it's not bad minus the horibad sample code ... which won't even work if you copy and paste it,

my advice find a different book there any many, many more accurate and detailed books on R and machine learning

10 people found this helpful

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W. Hogan

An interesting and easy read

Reviewed in the United States on December 12, 2013

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I enjoyed reading this book.

Pro's:
The text is parsimonious.
The examples are interesting.
The coding is clever.
The book is less expensive and easier to understand than most Springer texts.

Con's:
A substantial part of the code is peripheral tasks; this can be skipped.
Some of the code is out of date.

These Con's are trivial. The book is great. I would buy any other books written by these authors.

2 people found this helpful

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Amazon Customer

This book may be good for those with little mathematical or statistical background

Reviewed in the United States on October 7, 2016

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This book may be good for those with little mathematical or statistical background, but its background information sections are too long and its treatment of ML topics too superficial for the book to be very useful for someone with the requisite background to actually implement the methods described in the book.

2 people found this helpful

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Chris

Learned something new

Reviewed in the United States on October 2, 2012

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I learned a little about the R language and how a lot of scientist of physicist use it the write functions to dynamically create graphs to analyze the data they compiled. It also encompasses a lot of the same math you learn in statistics classes which makes sense. I even ended up downloading an R language reference app from the Apple app store to have an object reference of the language.

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Pete

Not for "hackers"

Reviewed in the United States on September 23, 2012

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As other reviews have noted, this book is R-heavy. And R turns out to be a poor choice for a lot of this data manipulation. As a "hacker" I'd prefer to use python or something similar for a lot of the code and then use R for the stuff that it's good at. Also the kindle formatting of this book is terrible. The code is formatted very poorly (I hope this is a kindle translation problem and not the way that it looks in the book). That poor formatting makes it difficult to read through the code. I'd recommend anyone who knows a decent programming language should try a different book.

9 people found this helpful

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Mr. A. J. Gould

Data science not machine learning

Reviewed in the United Kingdom on June 4, 2013

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This book isn't really about machine learning. There is relatively little in the book about the machine learning algorithms themselves. Most of it is plumbing: how to munge the data in using R. And there are some nice motivating examples on using the ggplot2 library in R to visualise the results. (However, on the downside, plots which require colour to be understood are presented in black and white, which doesn't help! The R code mostly works, so usually it is possible to produce your own, but it seems a waste of paper to output plots that show male vs female dots, say, in different shades of grey on the same graph.)

Also, when the algorithms are presented there are sometimes some serious errors (see the review on amazon's US site "Erroneous but entertaining" for more details). The single most shocking example was when a series of numbers was said to show the percentages of variation explained by an analysis, but the series added up to much than 100%. This was by no means the only error, however. The cumulative effect for me was that as I got further and further through the book, I began to have less and less trust in what was being presented to me.

I would characterise the book as being for hackers in the sense that you are encouraged to try a technique and see if it works. One good point is that the book emphasises having a separate test set from your training set.

Trying techniques until you find one that works is probably a good place to start, especially if your interest is in starting to learn the broader field of data science -- getting the data in, analysing it, visualising it -- rather than specialising in the selection and choice of machine learning algorithms themselves (for which Andrew Ng's coursera online course is a far better choice).

11 people found this helpful

Sai Sampath

The book is real good but I wouldn't prefer it if you are new ...

Reviewed in India on December 13, 2017

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The book is real good but I wouldn't prefer it if you are new to machine learning. You would find it to be real good if have basic knowledge of the subject

One person found this helpful

Carlo

Esauriente

Reviewed in Italy on May 3, 2017

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Un buon libro per chi si vuole avvicinare al Machine Learning, il taglio della spiegazione è americano quindi con esempi significativi.

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Martin Castillo Cabrera

Temas acordes

Reviewed in Mexico on April 12, 2019

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Excelente libro

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Kosako2007

Genial introducción al mundo del Machine Learning

Reviewed in Spain on February 23, 2013

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El libro es una muy buena introducción al Machine Learning y los conceptos en los que se basa. Lo recomiendo para todo aquel desarrollador que quiera introducirse en ese mundo.

One person found this helpful

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