Preface;
Machine Learning for Hackers;
How This Book Is Organized;
Conventions Used in This Book;
Using Code Examples;
Safari® Books Online;
How to Contact Us;
Acknowledgements;
Chapter 1: Using R;
1.1 R for Machine Learning;
Chapter 2: Data Exploration;
2.1 Exploration versus Confirmation;
2.2 What Is Data?;
2.3 Inferring the Types of Columns in Your Data;
2.4 Inferring Meaning;
2.5 Numeric Summaries;
2.6 Means, Medians, and Modes;
2.7 Quantiles;
2.8 Standard Deviations and Variances;
2.9 Exploratory Data Visualization;
2.10 Visualizing the Relationships Between Columns;
Chapter 3: Classification: Spam Filtering;
3.1 This or That: Binary Classification;
3.2 Moving Gently into Conditional Probability;
3.3 Writing Our First Bayesian Spam Classifier;
Chapter 4: Ranking: Priority Inbox;
4.1 How Do You Sort Something When You Don't Know the Order?;
4.2 Ordering Email Messages by Priority;
4.3 Writing a Priority Inbox;
Chapter 5: Regression: Predicting Page Views;
5.1 Introducing Regression;
5.2 Predicting Web Traffic;
5.3 Defining Correlation;
Chapter 6: Regularization: Text Regression;
6.1 Nonlinear Relationships Between Columns: Beyond Straight Lines;
6.2 Methods for Preventing Overfitting;
6.3 Text Regression;
Chapter 7: Optimization: Breaking Codes;
7.1 Introduction to Optimization;
7.2 Ridge Regression;
7.3 Code Breaking as Optimization;
Chapter 8: PCA: Building a Market Index;
8.1 Unsupervised Learning;
Chapter 9: MDS: Visually Exploring US Senator Similarity;
9.1 Clustering Based on Similarity;
9.2 How Do US Senators Cluster?;
Chapter 10: kNN: Recommendation Systems;
10.1 The k-Nearest Neighbors Algorithm;
10.2 R Package Installation Data;
Chapter 11: Analyzing Social Graphs;
11.1 Social Network Analysis;
11.2 Hacking Twitter Social Graph Data;
11.3 Analyzing Twitter Networks;
Chapter 12: Model Comparison;
12.1 SVMs: The Support Vector Machine;
12.2 Comparing Algorithms;
Works Citedbooks and publicationsbibliography ofresourcesbooks and publications; website resourcesstatisticsresources formachine learningresources forR programming languageresources for;
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