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Machine Learning for Hackers: Case Studies and Algorithms to Get You Started Paperback – Illustrated, 6 March 2012

by Drew Conway (Author), John Myles White (Contributor)
3.8 out of 5 stars 43
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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
Previous page
  1. Print length
    300 pages
  2. Language
    English
  3. Publisher
    O'Reilly Media, Inc, USA
  4. Publication date
    6 March 2012
  5. Dimensions
    17.78 x 1.91 x 23.34 cm
  6. ISBN-10
    1449303714
  7. ISBN-13
    978-1449303716
  8. See all details
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Product description

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 Media, Inc, USA; Illustrated edition (6 March 2012)
  • Language ‏ : ‎ English
  • Paperback ‏ : ‎ 300 pages
  • ISBN-10 ‏ : ‎ 1449303714
  • ISBN-13 ‏ : ‎ 978-1449303716
  • Dimensions ‏ : ‎ 17.78 x 1.91 x 23.34 cm
  • Customer Reviews:
    3.8 out of 5 stars 43

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3.8 out of 5 stars
3.8 out of 5
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Martin Castillo Cabrera
5.0 out of 5 stars Temas acordes
Reviewed in Mexico on 13 April 2019
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Excelente libro
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Sai Sampath
5.0 out of 5 stars The book is real good but I wouldn't prefer it if you are new ...
Reviewed in India on 13 December 2017
Verified Purchase
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
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Carlo
5.0 out of 5 stars Esauriente
Reviewed in Italy on 3 May 2017
Verified Purchase
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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W. Hogan
5.0 out of 5 stars An interesting and easy read
Reviewed in the United States on 12 December 2013
Verified Purchase
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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Bobobob
1.0 out of 5 stars Titel ist vorsätzliche Täuschung
Reviewed in Germany on 21 October 2014
Verified Purchase
Kann ich nur dringend von abraten. Klare vorsätzliche Täuschung der Titel. Es wir einem nicht erklärt was Machine Learning ist, es wird nur R benutzt die Algorithmen zu berechnen oder zu visualisieren und es wird weder R noch maschinelles Lernen erklärt.
7 people found this helpful
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