Machine learning for hackers
By: Drew Conway and John Myles White
Contributors: White, John Myles
Edition: 1st ed.
Publisher: O'Reilly Media
Published: 2012
Place of publication: Sebastopol, CA
Language: eng
Number of pages: 303
ISBN-13: 9781449303716
Subjects: Computer algorithms; Electronic data processing; Automation
Notes: "Case studies and algorithms to get you started"--Cover.

Includes bibliographic references (p.293-294) and index.
Table of contents: title: Machine generated contents note: 1. Using R; title: R for Machine Learning; title: Downloading and Installing R; title: IDEs and Text Editors; title: Loading and Installing R Packages; title: R Basics for Machine Learning; title: Further Reading on R; title: 2. Data Exploration; title: Exploration versus Confirmation; title: What Is Data?; title: Inferring the Types of Columns in Your Data; title: Inferring Meaning; title: Numeric Summaries; title: Means, Medians, and Modes; title: Quantiles; title: Standard Deviations and Variances; title: Exploratory Data Visualization; title: Visualizing the Relationships Between Columns; title: 3. Classification: Spam Filtering; title: This or That: Binary Classification; title: Moving Gently into Conditional Probability; title: Writing Our First Bayesian Spam Classifier; title: Defining the Classifier and Testing It with Hard Ham; title: Testing the Classifier Against All Email Types; title: Improving the Results; title: 4. Ranking: Priority Inbox; title: How Do You Sort Something When You Don't Know the Order?; title: Ordering Email Messages by Priority; title: Contents note continued: Priority Features of Email; title: Writing a Priority Inbox; title: Functions for Extracting the Feature Set; title: Creating a Weighting Scheme for Ranking; title: Weighting from Email Thread Activity; title: Training and Testing the Ranker; title: 5. Regression: Predicting Page Views; title: Introducing Regression; title: The Baseline Model; title: Regression Using Dummy Variables; title: Linear Regression in a Nutshell; title: Predicting Web Traffic; title: Defining Correlation; title: 6. Regularization: Text Regression; title: Nonlinear Relationships Between Columns: Beyond Straight Lines; title: Introducing Polynomial Regression; title: Methods for Preventing Overfitting; title: Preventing Overfitting with Regularization; title: Text Regression; title: Logistic Regression to the Rescue; title: 7. Optimization: Breaking Codes; title: Introduction to Optimization; title: Ridge Regression; title: Code Breaking as Optimization; title: 8. PCA: Building a Market Index; title: Unsupervised Learning; title: 9. MDS: Visually Exploring US Senator Similarity; title: Contents note continued: Clustering Based on Similarity; title: A Brief Introduction to Distance Metrics and Multidirectional Scaling; title: How Do US Senators Cluster?; title: Analyzing US Senator Roll Call Data (101st; title: 111th Congresses); title: 10. kNN: Recommendation Systems; title: The k-Nearest Neighbors Algorithm; title: R Package Installation Data; title: 11. Analyzing Social Graphs; title: Social Network Analysis; title: Thinking Graphically; title: Hacking Twitter Social Graph Data; title: Working with the Google SocialGraph API; title: Analyzing Twitter Networks; title: Local Community Structure; title: Visualizing the Clustered Twitter Network with Gephi; title: Building Your Own "Who to Follow" Engine; title: 12. Model Comparison; title: SVMs: The Support Vector Machine; title: Comparing Algorithms.