Shmueli G Machine Learning for Business Analytics Concepts, Tech and App 2023
Category: eBooks
- views: 247
- date: 15 February 2023
- posted by: BeMyLove
Shmueli G Machine Learning for Business Analytics Concepts, Tech and App 2023 | 102.31 MB
English | 702 Pages
Title: Data Mining for Business Analytics: Concepts, Techniques, and Applications in R
Author: Galit Shmueli
Year: 2023
Description:
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustrationReaders will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.
This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:
Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government
Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
More than a dozen case studies demonstrating applications for the data mining techniques described
End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions
DOWNLOAD:
https://rapidgator.net/file/e31c3384bf09a7ebd9635465d055893c/Shmueli_G._Machine_Learning_for_Business_Analytics._Concepts_Tech_and_App..2023.rar
https://uploadgig.com/file/download/58B9f1133C601aA8/Shmueli_G._Machine_Learning_for_Business_Analytics._Concepts_Tech_and_App..2023.rar
Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustrationReaders will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities.
This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes:
Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government
Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students
More than a dozen case studies demonstrating applications for the data mining techniques described
End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented
A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions
DOWNLOAD:
https://rapidgator.net/file/e31c3384bf09a7ebd9635465d055893c/Shmueli_G._Machine_Learning_for_Business_Analytics._Concepts_Tech_and_App..2023.rar
https://uploadgig.com/file/download/58B9f1133C601aA8/Shmueli_G._Machine_Learning_for_Business_Analytics._Concepts_Tech_and_App..2023.rar
We need your support!
Make a donation to help us stay online
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem
Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u
USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239
USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm
Information |
|||
 |
Users of GUESTS are not allowed to comment this publication. | ||