BookMachine learning for hackers
Personal name
Main title
- Machine learning for hackers / Drew Conway and John Myles White.
Edition
- 1st ed.
Published/Created
- Sebastopol, CA : O'Reilly Media, 2012.
Links
Links
- Contributor biographical information http://www.loc.gov/catdir/enhancements/fy1307/2012277057-b.html opens in a new window
- Publisher description http://www.loc.gov/catdir/enhancements/fy1307/2012277057-d.html opens in a new window
- Table of contents only http://www.loc.gov/catdir/enhancements/fy1307/2012277057-t.html opens in a new window
More Information
LCCN Permalink
Description
- xiii, 303 p. : ill. ; 24 cm.
ISBN
- 9781449303716
- 1449303714
LC classification
- QA76.9.A43 C674 2012
Related names
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.
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Notes
- "Case studies and algorithms to get you started"--Cover.
- Includes bibliographical references (p. 293-294) and index.
LCCN
- 2012277057
Dewey class no.
- 005.1
National bib agency no.
- 015952116
Other system no.
- (OCoLC)ocn783384312
Type of material
- Book
Item Availability
CALL NUMBER
- QA76.9.A43 C674 2012
- Copy 1
Request in
- Jefferson or Adams Building Reading Rooms
Status
- Not Charged
CALL NUMBER
- QA76.9.A43 C674 2012 LANDOVR
- Copy 2
Request in
- Jefferson or Adams Building Reading Rooms - STORED OFFSITE
Status
- Not Charged