If you’re an experienced programmer willing to crunch data, this concise guide will show you how to use machine learning to work with email. You’ll learn how to write algorithms that automatically sort and redirect email based on statistical patterns. Authors Drew Conway and John Myles White approach the process in a practical fashion, using a case-study driven approach rather than a traditional math-heavy presentation.
This book also includes a short tutorial on using the popular R language to manipulate and analyze data. You’ll get clear examples for analyzing sample data and writing machine learning programs with R.
- Mine email content with R functions, using a collection of sample files
- Analyze the data and use the results to write a Bayesian spam classifier
- Rank email by importance, using factors such as thread activity
- Use your email ranking analysis to write a priority inbox program
- Test your classifier and priority inbox with a separate email sample set
Table of contents
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
- R for Machine Learning
2. Data Exploration
- Exploration vs. Confirmation
- What is Data?
- Inferring the Types of Columns in Your Data
- Inferring Meaning
- Numeric Summaries
- Means, Medians, and Modes
- Standard Deviations and Variances
- Exploratory Data Visualization
- Visualizing the Relationships between Columns
- 3. Classification: Spam Filtering
4. Ranking: Priority Inbox
- How Do You Sort Something When You Don’t Know the Order?
- Ordering Email Messages by Priority
- Writing a Priority Inbox
- Works Cited
- About the Authors
- Title: Machine Learning for Email
- Release date: October 2011
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781449314309
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