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Machine learning and data analytics for solving business problemsmethods, applications, and case studies /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning and data analytics for solving business problemsedited by Bader Alyoubi ... [et al.].
Reminder of title:	methods, applications, and case studies /
other author:	Alyoubi, Bader.
Published:	Cham :Springer International Publishing :2022.
Description:	xii, 206 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-031-18483-3
ISBN:	9783031184833$q(electronic bk.)

Machine learning and data analytics for solving business problemsmethods, applications, and case studies /
Machine learning and data analytics for solving business problemsmethods, applications, and case studies /[electronic resource] :edited by Bader Alyoubi ... [et al.]. - Cham :Springer International Publishing :2022. - xii, 206 p. :ill., digital ;24 cm. - Unsupervised and semi-supervised learning,2522-8498. - Unsupervised and semi-supervised learning..

Introduction -- Supervised and unsupervised methods for customer segmentation -- Supervised and unsupervised methods for supply chain management -- Supervised and unsupervised methods for logistics improvement -- Design of recommender systems -- Supervised and unsupervised methods for e-marketing -- Analysis of Blockchain data -- Supervised and unsupervised methods applied in banking -- Cryptocurrency analysis -- Supervised and unsupervised methods to improve operational processes -- Big data analysis and summarization -- Intelligent Financial analysis and sales forecasting -- Financial data modeling and decision making -- Integration of learning methods in Smart ERP systems -- E-commerce recommender systems -- Social media and E-business analysis -- Intelligent Business control and monitoring systems -- Conclusion.

This book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes. These methods form the theoretical foundations of intelligent management systems, which allows for companies to understand the market environment, to improve the analysis of customer needs, to propose creative personalization of contents, and to design more effective business strategies, products, and services. This book gives an overview of recent methods - such as blockchain, big data, artificial intelligence, and cloud computing - so readers can rapidly explore them and their applications to solve common business challenges. The book aims to empower readers to leverage and develop creative supervised and unsupervised methods to solve business decision-making problems. Provides design and applications of machine learning and data analytics to solve business problems; Includes applications of supervised and unsupervised learning methods in intelligent management systems; Introduces case studies of business problems solved using innovative learning methods and data analytics techniques.

ISBN: 9783031184833$q(electronic bk.)

Standard No.: 10.1007/978-3-031-18483-3doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning and data analytics for solving business problemsmethods, applications, and case studies /
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245 0 0 $a Machine learning and data analytics for solving business problems $h [electronic resource] : $b methods, applications, and case studies / $c edited by Bader Alyoubi ... [et al.].
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505 0 $a Introduction -- Supervised and unsupervised methods for customer segmentation -- Supervised and unsupervised methods for supply chain management -- Supervised and unsupervised methods for logistics improvement -- Design of recommender systems -- Supervised and unsupervised methods for e-marketing -- Analysis of Blockchain data -- Supervised and unsupervised methods applied in banking -- Cryptocurrency analysis -- Supervised and unsupervised methods to improve operational processes -- Big data analysis and summarization -- Intelligent Financial analysis and sales forecasting -- Financial data modeling and decision making -- Integration of learning methods in Smart ERP systems -- E-commerce recommender systems -- Social media and E-business analysis -- Intelligent Business control and monitoring systems -- Conclusion.
520 $a This book presents advances in business computing and data analytics by discussing recent and innovative machine learning methods that have been designed to support decision-making processes. These methods form the theoretical foundations of intelligent management systems, which allows for companies to understand the market environment, to improve the analysis of customer needs, to propose creative personalization of contents, and to design more effective business strategies, products, and services. This book gives an overview of recent methods - such as blockchain, big data, artificial intelligence, and cloud computing - so readers can rapidly explore them and their applications to solve common business challenges. The book aims to empower readers to leverage and develop creative supervised and unsupervised methods to solve business decision-making problems. Provides design and applications of machine learning and data analytics to solve business problems; Includes applications of supervised and unsupervised learning methods in intelligent management systems; Introduces case studies of business problems solved using innovative learning methods and data analytics techniques.
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