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Machine learning with R : learn data cleansing to modeling from the tidyverse to neural networks and working with big data / Brett Lantz.

By: Material type: TextTextPublisher: Birmingham : Packt Publishing, 2023Edition: Fourth editionSubject(s): Additional physical formats: Print version :: No titleDDC classification:
  • 006.31 23 L-25
Contents:
Table of ContentsIntroducing Machine LearningManaging and Understanding DataLazy Learning – Classification Using Nearest NeighborsProbabilistic Learning – Classification Using Naive BayesDivide and Conquer – Classification Using Decision Trees and RulesForecasting Numeric Data – Regression MethodsBlack-Box Methods – Neural Networks and Support Vector MachinesFinding Patterns – Market Basket Analysis Using Association RulesFinding Groups of Data – Clustering with k-meansEvaluating Model PerformanceBeing Successful with Machine LearningAdvanced Data PreparationChallenging Data – Too Much, Too Little, Too ComplexBuilding Better LearnersMaking Use of Big Data.
Holdings
Item type Current library Collection Call number Status Date due Barcode
Textbook Textbook GMIT Library Information Sciences English book 006.31 L-25 (Browse shelf(Opens below)) Available 3334-1
Textbook Textbook GMIT Library Information Sciences English book 006.31 L-25 (Browse shelf(Opens below)) Available 3334-2
Textbook Textbook GMIT Library Information Sciences English book 006.31 L-25 (Browse shelf(Opens below)) Available 3334-3

Print on demand edition.

Previous edition: 2019.

Includes bibliographical references and index.

Table of ContentsIntroducing Machine LearningManaging and Understanding DataLazy Learning – Classification Using Nearest NeighborsProbabilistic Learning – Classification Using Naive BayesDivide and Conquer – Classification Using Decision Trees and RulesForecasting Numeric Data – Regression MethodsBlack-Box Methods – Neural Networks and Support Vector MachinesFinding Patterns – Market Basket Analysis Using Association RulesFinding Groups of Data – Clustering with k-meansEvaluating Model PerformanceBeing Successful with Machine LearningAdvanced Data PreparationChallenging Data – Too Much, Too Little, Too ComplexBuilding Better LearnersMaking Use of Big Data.

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