Machine Learning for Hackers--New from O'Reilly Media

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For Immediate Release
For more information, please contact:
Gretchen Giles (707) 827-7136, gretchen@oreilly.com, @oreillymedia

Machine Learning for Hackers--New from O'Reilly Media
Case Studies and Algorithms to Get You Started

Sebastopol, CA--If you're an experienced programmer interested in crunching data, "Machine Learning
for Hackers" (O'Reilly Media, $39.99 USD) 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.

"We can see how many people are interested in learning about machine learning (ML), but don't have
the mathematical background to read traditional treatments of the book," says White
(@johnmyleswhite). "We wanted to get people interested in ML in a hands-on fashion in the way that
chemistry sets can get children excited about chemistry before they have the scientific background
to learn the subject rigorously."

White says that he and coauthor Drew Conway (@drewconway) wrote the book to match the tech
community's growing interest in ML.

He explains: "Our intended audience is anyone with a solid background in computing programming and
a quantitative mind, but no formal training in advanced mathematics. For people who are experts in
calculus and linear algebra, the traditional books on machine learning are probably more
appropriate. But we find that most people we meet don't have a strong enough command of those
topics to learn ML from the traditional books in a timely fashion."

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

For a review copy or more information please email gretchen@oreilly.com. Please include your
delivery address and contact information.

About the Authors
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.

Additional Resources
For more information about the book, including table of contents, author bios, and cover graphic,
see:
http://post.oreilly.com/rd/9z1zdr9ivfq0mqir7lon49gada5ts1...

Machine Learning for Hackers
Publisher: O'Reilly Media
By Drew Conway, John Myles White
Print ISBN: 9781449303716
Pages: 322
Print Price: $39.99, Ebook Price: $31.99
order@oreilly.com
1-800-998-9938
1-707-827-7000

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