Coverart for item
The Resource Machine learning for email, Drew Conway and John Myles White

Machine learning for email, Drew Conway and John Myles White

Label
Machine learning for email
Title
Machine learning for email
Statement of responsibility
Drew Conway and John Myles White
Creator
Contributor
Subject
Language
eng
Summary
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
Cataloging source
UMI
http://library.link/vocab/creatorName
Conway, Drew
Dewey number
004.692
Illustrations
illustrations
Index
no index present
LC call number
TK5105.73
LC item number
.C66 2012eb
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
http://library.link/vocab/relatedWorkOrContributorName
White, John Myles
http://library.link/vocab/subjectName
  • Electronic mail messages
  • Electronic mail systems
  • Spam (Electronic mail)
  • Spam filtering (Electronic mail)
  • Machine learning
  • COMPUTERS
  • COMPUTERS
  • Electronic mail systems
  • Machine learning
  • Spam filtering (Electronic mail)
Label
Machine learning for email, Drew Conway and John Myles White
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references (pages 129-130)
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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 vs. 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 -- Modes -- Skewness -- Thin Tails vs. Heavy Tails -- 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 -- Priority Features 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
Control code
778357024
Dimensions
unknown
Edition
1st ed.
Extent
1 online resource (xi, 130 pages)
File format
unknown
Form of item
online
Isbn
9781449320706
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations
http://library.link/vocab/ext/overdrive/overdriveId
edcb53f1-244c-471f-83f9-c3de9fbd13a7
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)778357024
Label
Machine learning for email, Drew Conway and John Myles White
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references (pages 129-130)
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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 vs. 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 -- Modes -- Skewness -- Thin Tails vs. Heavy Tails -- 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 -- Priority Features 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
Control code
778357024
Dimensions
unknown
Edition
1st ed.
Extent
1 online resource (xi, 130 pages)
File format
unknown
Form of item
online
Isbn
9781449320706
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations
http://library.link/vocab/ext/overdrive/overdriveId
edcb53f1-244c-471f-83f9-c3de9fbd13a7
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
(OCoLC)778357024

Library Locations

    • Ellis LibraryBorrow it
      1020 Lowry Street, Columbia, MO, 65201, US
      38.944491 -92.326012
    • Engineering Library & Technology CommonsBorrow it
      W2001 Lafferre Hall, Columbia, MO, 65211, US
      38.946102 -92.330125
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