Machine Learning for Hackers

1. Using R
-R for Machine Learning

2. Data Exploration
-Exploration versus Confirmation
-What Is Data?
-Inferring the Types of Columns in Your Data
-Inferring Meaning
-Numeric Summaries
-Means, Medians, and Modes
-Quantiles
-Standard Deviations and Variances
-Exploratory Data Visualization
-Visualizing the Relationships Between Columns

3. Classification: Spam Filtering
-This or That: Binary Classification
-Moving Gently into Conditional Probability
-Writing Our First Bayesian Spam Classifier

4. Ranking: Priority Inbox
-How Do You Sort Something When You Don't Know the Order?
-Ordering Email Messages by Priority
-Writing a Priority Inbox

5. Regression: Predicting Page Views
-Introducing Regression
-Predicting Web Traffic
-Defining Correlation

6. Regularization: Text Regression
-Nonlinear Relationships Between Columns: Beyond Straight Lines
-Methods for Preventing Overfitting
-Text Regression

7. Optimization: Breaking Codes
-Introduction to Optimization
-Ridge Regression
-Code Breaking as Optimization

8. PCA: Building a Market Index
-Unsupervised Learning

9. MDS: Visually Exploring US Senator Similarity
-Clustering Based on Similarity
-How Do US Senators Cluster?

10. kNN: Recommendation Systems
-The k-Nearest Neighbors Algorithm
-R Package Installation Data

11. Analyzing Social Graphs
-Social Network Analysis
-Hacking Twitter Social Graph Data
-Analyzing Twitter Networks

12. Model Comparison
-SVMs: The Support Vector Machine
-Comparing Algorithms

Works Cited
Index