Machine Learning for Hackers

Купить бумажную книгу и читать

Название: Machine Learning for Hackers

Год:2012

SBN: 1449303714

Издательство:ORM

Формат: pdf

Страниц:322

Язык: английский

Размер:23 мб

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