Machine learning for practical decision making: a multidisciplinary perspective with applications from healthcare, engineering and business analytics

By:

Morr, Christo El

Contributor(s):

Material type: Text

TextPublication details: Springer Switzerland 2022Description: xvii, 465 pISBN:

9783031169892

Subject(s):

DDC classification:

006.31 MOR

Summary: This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines. The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.

List(s) this item appears in: Fiction

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31 MOR (Browse shelf(Opens below))	1	Available		004650

This book provides a hands-on introduction to Machine Learning (ML) from a multidisciplinary perspective that does not require a background in data science or computer science. It explains ML using simple language and a straightforward approach guided by real-world examples in areas such as health informatics, information technology, and business analytics. The book will help readers understand the various key algorithms, major software tools, and their applications. Moreover, through examples from the healthcare and business analytics fields, it demonstrates how and when ML can help them make better decisions in their disciplines.

The book is chiefly intended for undergraduate and graduate students who are taking an introductory course in machine learning. It will also benefit data analysts and anyone interested in learning ML approaches.

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