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Machine learning for practical decis...

El Morr, Christo.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for practical decision making/ by Christo El Morr ... [et al.].
其他題名:	a multidisciplinary perspective with applications from healthcare, engineering and business analytics /
其他作者:	El Morr, Christo.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xvii, 465 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-3-031-16990-8
ISBN:	9783031169908

Machine learning for practical decision making = a 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. 334..

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

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

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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