Machine Learning for Hackers

Table of contents

1. Machine Learning for Hackers
2. Preface
   1. Machine Learning for Hackers
   2. How This Book Is Organized
   3. Conventions Used in This Book
   4. Using Code Examples
   5. Safari® Books Online
   6. How to Contact Us
   7. Acknowledgements
3. 1. Using R
   1. R for Machine Learning
      1. Downloading and Installing R
         1. Windows
         2. Mac OS X
         3. Linux
      2. IDEs and Text Editors
      3. Loading and Installing R Packages
      4. R Basics for Machine Learning
         1. Loading libraries and the data
         2. Converting date strings and dealing with malformed data
         3. Organizing location data
         4. Dealing with data outside our scope
         5. Aggregating and organizing the data
         6. Analyzing the data
      5. Further Reading on R
4. 2. Data Exploration
   1. Exploration versus Confirmation
   2. What Is Data?
   3. Inferring the Types of Columns in Your Data
   4. Inferring Meaning
   5. Numeric Summaries
   6. Means, Medians, and Modes
   7. Quantiles
   8. Standard Deviations and Variances
   9. Exploratory Data Visualization
   10. Visualizing the Relationships Between Columns
5. 3. Classification: Spam Filtering
   1. This or That: Binary Classification
   2. Moving Gently into Conditional Probability
   3. Writing Our First Bayesian Spam Classifier
      1. Defining the Classifier and Testing It with Hard Ham
      2. Testing the Classifier Against All Email Types
      3. Improving the Results
6. 4. Ranking: Priority Inbox
   1. How Do You Sort Something When You Don’t Know the Order?
   2. Ordering Email Messages by Priority
      1. Priority Features of Email
   3. Writing a Priority Inbox
      1. Functions for Extracting the Feature Set
      2. Creating a Weighting Scheme for Ranking
         1. A log-weighting scheme
      3. Weighting from Email Thread Activity
      4. Training and Testing the Ranker
7. 5. Regression: Predicting Page Views
   1. Introducing Regression
      1. The Baseline Model
      2. Regression Using Dummy Variables
      3. Linear Regression in a Nutshell
   2. Predicting Web Traffic
   3. Defining Correlation
8. 6. Regularization: Text Regression
   1. Nonlinear Relationships Between Columns: Beyond Straight Lines
      1. Introducing Polynomial Regression
   2. Methods for Preventing Overfitting
      1. Preventing Overfitting with Regularization
   3. Text Regression
      1. Logistic Regression to the Rescue
9. 7. Optimization: Breaking Codes
   1. Introduction to Optimization
   2. Ridge Regression
   3. Code Breaking as Optimization
10. 8. PCA: Building a Market Index
    1. Unsupervised Learning
11. 9. MDS: Visually Exploring US Senator Similarity
    1. Clustering Based on Similarity
       1. A Brief Introduction to Distance Metrics and Multidirectional Scaling
    2. How Do US Senators Cluster?
       1. Analyzing US Senator Roll Call Data (101st–111th Congresses)
          1. Exploring senator MDS clustering by Congress
12. 10. kNN: Recommendation Systems
    1. The k-Nearest Neighbors Algorithm
    2. R Package Installation Data
13. 11. Analyzing Social Graphs
    1. Social Network Analysis
       1. Thinking Graphically
    2. Hacking Twitter Social Graph Data
       1. Working with the Google SocialGraph API
    3. Analyzing Twitter Networks
       1. Local Community Structure
       2. Visualizing the Clustered Twitter Network with Gephi
       3. Building Your Own “Who to Follow” Engine
14. 12. Model Comparison
    1. SVMs: The Support Vector Machine
    2. Comparing Algorithms
15. Works Cited
    1. Books
    2. Articles
16. Index
17. About the Authors
18. Colophon
19. Copyright