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Machine Learning for Hackers: Case Studies and Algorithms to Get You Started 1st Edition
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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
- ISBN-101449303714
- ISBN-13978-1449303716
- Edition1st
- PublisherO'Reilly
- Publication dateMarch 6, 2012
- LanguageEnglish
- Dimensions7 x 0.75 x 9.19 inches
- Print length324 pages
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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: #443,672 in Books (See Top 100 in Books)
- #82 in Natural Language Processing (Books)
- #84 in Machine Theory (Books)
- #361 in Computer Hacking
- Customer Reviews:
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There is way too much time spent on R, dedicated to such things as parsing email messages, and spidering webpages, etc. These are things that no-one with other tools available would do in R. And it's not that it's easier to do it in R, it's actually harder than using an appropriate library, like JavaMail. And yet, while much time is spent in details, like regexes to extract dates (ick!), more interesting R functions are given short shrift.
There's some good material in here, but it's buried under the weight of doing everything in R. If you are a non-programmer, and want to use only one hammer for everything, then R is not a bad choice. But it's not a good choice for developers that are already comfortable with a wider variety of tools.
I'd recommend Programming Collective Intelligence by Segaran, if you would describe yourself as a "Hacker".
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.
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.
Top reviews from other countries

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).



