MARC Record from Library of Congress

LEADER: 04039cam a22003737a 4500
001 2012277057
003 DLC
005 20130523092642.0
008 130227s2012 caua b 001 0 eng
010 $a 2012277057
016 7 $a015952116$2Uk
020 $a9781449303716
020 $a1449303714
035 $a(OCoLC)ocn783384312
040 $aAU@$beng$cAU@$dISM$dYDXCP$dCDX$dIUL$dBTCTA$dUKMGB$dBDX$dOCLCQ$dDLC
042 $alccopycat
050 00 $aQA76.9.A43$bC674 2012
082 04 $a005.1$223
100 1 $aConway, Drew.
245 10 $aMachine learning for hackers /$cDrew Conway and John Myles White.
250 $a1st ed.
260 $aSebastopol, CA :$bO'Reilly Media,$c2012.
300 $axiii, 303 p. :$bill. ;$c24 cm.
500 $a"Case studies and algorithms to get you started"--Cover.
504 $aIncludes bibliographical references (p. 293-294) and index.
505 0 $aMachine 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.
505 0 $aContents 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.
505 0 $aContents 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.
650 0 $aComputer algorithms.
650 0 $aElectronic data processing$xAutomation.
700 1 $aWhite, John Myles.
856 42 $3Contributor biographical information$uhttp://www.loc.gov/catdir/enhancements/fy1307/2012277057-b.html
856 42 $3Publisher description$uhttp://www.loc.gov/catdir/enhancements/fy1307/2012277057-d.html
856 41 $3Table of contents only$uhttp://www.loc.gov/catdir/enhancements/fy1307/2012277057-t.html