Machine Learning for Hackers

Machine Learning for Hackers

 English | PDF | 20 MB | 322 pages

 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

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