Description 
1 online resource (xiii, 303 pages) : illustrations 
Contents 
Table of Contents; Preface; Machine Learning for Hackers; How This Book Is Organized; Conventions Used in This Book; Using Code Examples; SafariĀ® Books Online; How to Contact Us; Acknowledgements; Chapter 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 

A Brief Introduction to Distance Metrics and Multidirectional ScalingHow Do US Senators Cluster?; Analyzing US Senator Roll Call Data (101st111th Congresses); Exploring senator MDS clustering by Congress; Chapter 10. kNN: Recommendation Systems; The kNearest Neighbors Algorithm; R Package Installation Data; Chapter 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 

Further Reading on RChapter 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; Chapter 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 

Predicting Web TrafficDefining Correlation; Chapter 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; Chapter 7. Optimization: Breaking Codes; Introduction to Optimization; Ridge Regression; Code Breaking as Optimization; Chapter 8. PCA: Building a Market Index; Unsupervised Learning; Chapter 9. MDS: Visually Exploring US Senator Similarity; Clustering Based on Similarity 

Testing the Classifier Against All Email TypesImproving the Results; Chapter 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 logweighting scheme; Weighting from Email Thread Activity; Training and Testing the Ranker; Chapter 5. Regression: Predicting Page Views; Introducing Regression; The Baseline Model; Regression Using Dummy Variables; Linear Regression in a Nutshell 
Summary 
If you're an experienced programmer interested in crunching data, this book will get you started with machine learninga 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 handson case studies, instead of a traditional mathheavy 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 s 
Bibliography 
Includes bibliographical references (pages 293294) and index 
Notes 
Print version record 
In 
EBL 
Subject 
Electronic Data Processing.


Programming Languages.


Social Networking.


Artificial Intelligence.


Computer Graphics.


Software.


Computer algorithms.


Electronic data processing  Automation.

Genre/Form 
Statistics.

Form 
Electronic book

Author 
White, John Myles.

ISBN 
1449330533 (electronic bk.) 

1449330541 (electronic bk.) 

9781449330538 (electronic bk.) 

9781449330545 (electronic bk.) 
