Machine Learning for Hackers

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

Table of Contents
Chapter 1. Using R
Chapter 2. Data Exploration
Chapter 3. Classification: Spam Filtering
Chapter 4. Ranking: Priority Inbox
Chapter 5. Regression: Predicting Page Views
Chapter 6. Regularization: Text Regression
Chapter 7. Optimization: Breaking Codes
Chapter 8. PCA: Building a Market Index
Chapter 9. MDS: Visually Exploring US Senator Similarity
Chapter 10. kNN: Recommendation Systems
Chapter 11. Analyzing Social Graphs
Chapter 12. Model Comparison

Book Details

  • Paperback: 322 pages
  • Publisher: O’Reilly Media (February 2012)
  • Language: English
  • ISBN-10: 1449303714
  • ISBN-13: 978-1449303716
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