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＜電子ブック＞
Machine Learning for Practical Decision Making : A Multidisciplinary Perspective with Applications from Healthcare, Engineering and Business Analytics / by Christo El Morr, Manar Jammal, Hossam Ali-Hassan, Walid EI-Hallak
(International Series in Operations Research & Management Science. ISSN:22147934 ; 334)

版	1st ed. 2022.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2022
本文言語	英語
大きさ	XVII, 465 p. 1 illus : online resource
著者標目	*El Morr, Christo author Jammal, Manar author Ali-Hassan, Hossam author EI-Hallak, Walid author SpringerLink (Online service)
件　名	LCSH:Operations research LCSH:Health services administration LCSH:Medical informatics LCSH:Machine learning LCSH:Artificial intelligence LCSH:Business -- Data processing  全ての件名で検索 FREE:Operations Research and Decision Theory FREE:Health Care Management FREE:Health Informatics FREE:Machine Learning FREE:Artificial Intelligence FREE:Business Analytics
一般注記	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 HTTP:URL=https://doi.org/10.1007/978-3-031-16990-8

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