Machine learning with R : expert techniques for predictive modeling
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Format
Edition
Third edition.
Language
English
ISBN
9781523125241, 1523125241, 9781788291552, 1788291557, 1788295862, 9781788295864
UPC
9781788295864
Notes
General Note
Includes index.
Bibliography
Includes bibliographical references and index.
Restrictions on Access
Legal Deposit;,Only available on premises controlled by the deposit library and to one user at any one time;,The Legal Deposit Libraries (Non-Print Works) Regulations (UK).,WlAbNL
Description
A hands-on, readable guide to machine learning with R. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights and make new predictions. The 3rd edition features newer and better libraries, advice on ethical and bias issues, and an introduction to deep learning.
Terms Governing Use and Reproduction
Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.,WlAbNL
Local note
O'Reilly,O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Lantz, B. (2019). Machine learning with R: expert techniques for predictive modeling (Third edition.). Packt.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Lantz, Brett. 2019. Machine Learning With R: Expert Techniques for Predictive Modeling. Birmingham, UK: Packt.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Lantz, Brett. Machine Learning With R: Expert Techniques for Predictive Modeling Birmingham, UK: Packt, 2019.
Harvard Citation (style guide)Lantz, B. (2019). Machine learning with R: expert techniques for predictive modeling. Third edn. Birmingham, UK: Packt.
MLA Citation, 9th Edition (style guide)Lantz, Brett. Machine Learning With R: Expert Techniques for Predictive Modeling Third edition., Packt, 2019.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
Staff View
Grouped Work ID
e870c18b-d360-69a6-c71d-e0a89ccbd492-eng
Grouping Information
| Grouped Work ID | e870c18b-d360-69a6-c71d-e0a89ccbd492-eng |
|---|---|
| Full title | machine learning with r expert techniques for predictive modeling |
| Author | lantz brett |
| Grouping Category | book |
| Last Update | 2024-12-17 08:30:41AM |
| Last Indexed | 2024-12-17 08:34:34AM |
Book Cover Information
| Image Source | default |
|---|---|
| First Loaded | Aug 13, 2023 |
| Last Used | Nov 4, 2024 |
Marc Record
| First Detected | Mar 21, 2023 01:15:15 PM |
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| Last File Modification Time | Dec 17, 2024 08:09:42 AM |
| Suppressed | Record had no items |
MARC Record
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| 245 | 1 | 0 | |a Machine learning with R :|b expert techniques for predictive modeling /|c Brett Lantz. |
| 250 | |a Third edition. | ||
| 264 | 1 | |a Birmingham, UK :|b Packt,|c [2019] | |
| 300 | |a 1 online resource (xiii, 437 pages) :|b illustrations | ||
| 336 | |a text|b txt|2 rdacontent | ||
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| 490 | 1 | |a Expert insight | |
| 500 | |a Includes index. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Introducing machine learning -- Managing and understanding data -- Lazy learning -- classification using nearest neighbors -- Probabilistic learning -- classification using naive Bayes -- Divide and conquer -- classification using decision trees and rules -- Forecasting numeric data -- regression methods -- Black box methods -- neural networks and support vector machines -- Finding patterns -- market basket analysis using association rules -- Finding groups of data -- clustering with k-means -- Evaluation model performance -- Improving model performance -- Specialized machine learning topics. | |
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| 520 | |a A hands-on, readable guide to machine learning with R. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights and make new predictions. The 3rd edition features newer and better libraries, advice on ethical and bias issues, and an introduction to deep learning. | ||
| 540 | |a Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.|5 WlAbNL | ||
| 590 | |a O'Reilly|b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning.|9 46043 | |
| 650 | 0 | |a R (Computer program language)|9 74517 | |
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| 830 | 0 | |a Expert insight. | |
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