Machine learning for hackers / Drew Conway and John Myles White ; editor, Julie Steele ; illustrator, Robert Romano.

Format:

Book

Author/Creator:

Conway, Drew.

Contributor:

White, John Myles.

Steele, Julie.

Romano, Robert (Illustrator), illustrator.

Language:

English

Subjects (All):

Computer algorithms.

Electronic data processing--Automation.

Electronic data processing.

Physical Description:

1 online resource (322 p.)

Edition:

First edition.

Place of Publication:

Sebastopol, California : O'Reilly Media, 2012.

Language Note:

English

System Details:

text file

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 h

Contents:

Machine generated contents note: 1. Using R

R for Machine Learning

Downloading and Installing R

IDEs and Text Editors

Loading and Installing R Packages

R Basics for Machine Learning

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.

Contents note continued: Priority Features of Email

Writing a Priority Inbox

Functions for Extracting the Feature Set

Creating a Weighting Scheme for Ranking

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.

Contents note continued: 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)

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.

Notes:

"Case studies and algorithms to get you started"--Cover.

Includes bibliographical references (pages 293-294) and index.

Description based on print version record.

ISBN:

9781306812603

1306812607

9781449330538

1449330533

9781449330545

1449330541

OCLC:

780425806