MARC details
| 000 -LEADER |
|---|
| fixed length control field |
03757cam a22003377a 4500 |
| CONTROL NUMBER |
|---|
| control field |
17640128 |
| DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20190415102858.0 |
| FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
130227s2012 caua b 001 0 eng |
| LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2012277057 |
| NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER |
|---|
| Record control number |
015952116 |
| Source |
Uk |
| INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789350236741 |
| SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)ocn783384312 |
| CATALOGING SOURCE |
|---|
| Original cataloging agency |
AU@ |
| Language of cataloging |
eng |
| Transcribing agency |
AU@ |
| Modifying agency |
ISM |
| -- |
YDXCP |
| -- |
CDX |
| -- |
IUL |
| -- |
BTCTA |
| -- |
UKMGB |
| -- |
BDX |
| -- |
OCLCQ |
| -- |
DLC |
| AUTHENTICATION CODE |
|---|
| Authentication code |
lccopycat |
| LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.9.A43 |
| Item number |
C674 2012 |
| DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Edition number |
23 |
| Item number |
CON |
| MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Conway, Drew. |
| TITLE STATEMENT |
|---|
| Title |
Machine learning for hackers / |
| Statement of responsibility, etc |
Drew Conway and John Myles White. |
| PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Mumbai : |
| Name of publisher, distributor, etc |
O'Reilly, |
| -- |
Shroff Publishers & Distributors, |
| Date of publication, distribution, etc |
2018. |
| PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 303 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| GENERAL NOTE |
|---|
| General note |
"Case studies and algorithms to get you started"--Cover. |
| BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references (p. 293-294) and index. |
| FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer algorithms. |
| SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Electronic data processing |
| General subdivision |
Automation. |
| SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning. |
| ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
White, John Myles. |
| ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Books |
