Machine Learning for Hackers by Conway, Drew -- Read -- Imperial Library of Trantor

Index

Machine Learning for Hackers 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

1. Using R

R for Machine Learning

Downloading and Installing R

Windows Mac OS X Linux

IDEs and Text Editors Loading and Installing R Packages R Basics for Machine Learning

Loading libraries and the data Converting date strings and dealing with malformed data Organizing location data Dealing with data outside our scope Aggregating and organizing the data Analyzing the data

Further Reading on R

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

Defining the Classifier and Testing It with Hard Ham Testing the Classifier Against All Email Types Improving the Results

4. Ranking: Priority Inbox

How Do You Sort Something When You Don’t Know the Order? Ordering Email Messages by Priority

Priority Features of Email

Writing a Priority Inbox

Functions for Extracting the Feature Set Creating a Weighting Scheme for Ranking

A log-weighting scheme

Weighting from Email Thread Activity Training and Testing the Ranker

5. Regression: Predicting Page Views

Introducing Regression

The Baseline Model Regression Using Dummy Variables Linear Regression in a Nutshell

Predicting Web Traffic Defining Correlation

6. Regularization: Text Regression

Nonlinear Relationships Between Columns: Beyond Straight Lines

Introducing Polynomial Regression

Methods for Preventing Overfitting

Preventing Overfitting with Regularization

Text Regression

Logistic Regression to the Rescue

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

A Brief Introduction to Distance Metrics and Multidirectional Scaling

How Do US Senators Cluster?

Analyzing US Senator Roll Call Data (101st–111th Congresses)

Exploring senator MDS clustering by Congress

10. kNN: Recommendation Systems

The k-Nearest Neighbors Algorithm R Package Installation Data

11. Analyzing Social Graphs

Social Network Analysis

Thinking Graphically

Hacking Twitter Social Graph Data

Working with the Google SocialGraph API

Analyzing Twitter Networks

Local Community Structure Visualizing the Clustered Twitter Network with Gephi Building Your Own “Who to Follow” Engine

12. Model Comparison

SVMs: The Support Vector Machine Comparing Algorithms

Works Cited

Books Articles

Index About the Authors Colophon Copyright

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