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Machine learning with R : expert techniques for predictive modeling / Brett Lantz.

By: Material type: TextTextSeries: Expert insightPublisher: Birmingham, UK : Packt, [2019]Edition: Third editionDescription: 1 online resource (xiii, 437 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781523125241
  • 1523125241
  • 9781788291552
  • 1788291557
  • 1788295862
  • 9781788295864
Subject(s): Genre/Form: Additional physical formats: Print version:: Machine learning with RDDC classification:
  • 006.31 23
LOC classification:
  • Q325.5 .L367 2019
Online resources:
Contents:
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.
Summary: 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.
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Item type Home library Collection Call number Materials specified Status Date due Barcode
Electronic-Books Electronic-Books OPJGU Sonepat- Campus E-Books EBSCO Available

Includes index.

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.

Includes bibliographical references and index.

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.

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

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

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