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Book Cover
Author Conway, Drew.

Title Machine learning for hackers / Drew Conway and John Myles White
Edition First edition
Published Sebastopol, CA : O'Reilly, 2012
Online access available from:
ProQuest Ebook Central (owned titles)    View Resource Record  
Safari O'Reilly books online    View Resource Record  


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 (101st-111th Congresses); Exploring senator MDS clustering by Congress; Chapter 10. kNN: Recommendation Systems; The k-Nearest 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 log-weighting 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 learning--a 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 hands-on case studies, instead of a traditional math-heavy 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 293-294) and index
Notes Print version record
Subject Electronic Data Processing.
Programming Languages.
Social Networking.
Artificial Intelligence.
Computer Graphics.
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.)