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

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

Machine Learning for Hackers: Case Studies and Algorithms to Get You Started
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Machine Learning for Hackers: Case Studies and Algorithms to Get You Started Paperback - 2012

by White, John Myles

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Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data. Readers learn machine learning and statistics tools in a practical fashion, using black-box solutions and case studies instead of a traditional math-heavy presentation.

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paperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
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Details

  • Title Machine Learning for Hackers: Case Studies and Algorithms to Get You Started
  • Author White, John Myles
  • Binding Paperback
  • Edition INTERNATIONAL ED
  • Condition Used - Good
  • Pages 320
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2012-03-20
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index, Price on Product - Canadian, Table of Contents
  • Bookseller's Inventory # 1449303714.G
  • ISBN 9781449303716 / 1449303714
  • Weight 1.14 lbs (0.52 kg)
  • Dimensions 9.2 x 7.06 x 0.75 in (23.37 x 17.93 x 1.91 cm)
  • Category Computers - Languages / Programming
  • Library of Congress subjects Computer algorithms, Electronic data processing - Automation
  • Dewey Decimal Code 005.1
  • Quantity available 1

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From the publisher

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 nave 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

Media reviews

Citations

  • Reference and Research Bk News, 04/01/2012, Page 190

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.

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