Produktbild: Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021)

Machine Learning and Big Data Analytics (Proceedings of International Conference on Machine Learning and Big Data Analytics (ICMLBDA) 2021)

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Lecture Notes in Networks and Systems 256

ISBN: 3030824683
ISBN 13: 9783030824686
Herausgeber: Rajiv Misra/Rudrapatna K Shyamasundar/Amrita Chaturvedi et al
Verlag: Springer Verlag GmbH
Umfang: xi, 362 S., 79 s/w Illustr., 147 farbige Illustr., 362 p. 226 illus., 147 illus. in color.
Erscheinungsdatum: 30.09.2021
Auflage: 1/2022
Produktform: Kartoniert
Einband: Kartoniert

This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data sets-i.e., big data-to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.

Artikelnummer: 2576948 Kategorie:

Beschreibung

This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2021) is intended to be used as a reference book for researchers and practitioners in the disciplines of computer science, electronics and telecommunication, information science, and electrical engineering. Machine learning and Big data analytics represent a key ingredients in the industrial applications for new products and services. Big data analytics applies machine learning for predictions by examining large and varied data setsi.e., big datato uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make more informed business decisions.

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E-Mail: juergen.hartmann@springer.com

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