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Machine learning for practical decision makinga multidisciplinary perspective with applications from healthcare, engineering and business analytics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning for practical decision makingby Christo El Morr ... [et al.].
Reminder of title:	a multidisciplinary perspective with applications from healthcare, engineering and business analytics /
other author:	El Morr, Christo.
Published:	Cham :Springer International Publishing :2022.
Description:	xvii, 465 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-031-16990-8
ISBN:	9783031169908$q(electronic bk.)

Machine learning for practical decision makinga multidisciplinary perspective with applications from healthcare, engineering and business analytics /
Machine learning for practical decision makinga multidisciplinary perspective with applications from healthcare, engineering and business analytics /[electronic resource] :by Christo El Morr ... [et al.]. - Cham :Springer International Publishing :2022. - xvii, 465 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3342214-7934 ;. - International series in operations research & management science ;v. 140..

1. Introduction to Machine Learning -- 2. Statistics -- 3. Overview of Machine Learning Algorithms -- 4. Data Preprocessing -- 5. Data Visualization -- 6. Linear Regression -- 7. Logistic Regression -- 8. Decision Trees -- 9. Naïve Bayes -- 10. K-Nearest Neighbors -- 11. Neural Networks -- 12. K-Means -- 13. Support Vector Machine -- 14. Voting and Bagging -- 15. Boosting and Stacking -- 16. Future Directions and Ethical Considerations.

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.

ISBN: 9783031169908$q(electronic bk.)

Standard No.: 10.1007/978-3-031-16990-8doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for practical decision makinga multidisciplinary perspective with applications from healthcare, engineering and business analytics /
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245 0 0 $a Machine learning for practical decision making $h [electronic resource] : $b a multidisciplinary perspective with applications from healthcare, engineering and business analytics / $c by Christo El Morr ... [et al.].
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300 $a xvii, 465 p. : $b ill., digital ; $c 24 cm.
490 1 $a International series in operations research & management science, $x 2214-7934 ; $v v. 334
505 0 $a 1. Introduction to Machine Learning -- 2. Statistics -- 3. Overview of Machine Learning Algorithms -- 4. Data Preprocessing -- 5. Data Visualization -- 6. Linear Regression -- 7. Logistic Regression -- 8. Decision Trees -- 9. Naïve Bayes -- 10. K-Nearest Neighbors -- 11. Neural Networks -- 12. K-Means -- 13. Support Vector Machine -- 14. Voting and Bagging -- 15. Boosting and Stacking -- 16. Future Directions and Ethical Considerations.
520 $a 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.
650 0 $a Machine learning. $3 188639
650 0 $a Decision making $x Data processing. $3 235779
700 1 $a El Morr, Christo. $3 837089
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