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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
|Publisher:||O'Reilly Media, Incorporated|
|Product dimensions:||7.00(w) x 9.10(h) x 0.80(d)|
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.
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
- Works Citedbooks and publicationsbibliography ofresourcesbooks and publications; website resourcesstatisticsresources formachine learningresources forR programming languageresources for