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Machine learning crash course for engineers

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning crash course for engineers/ by Eklas Hossain.
Author:	Hossain, Eklas.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xx, 453 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	Introduction to Machine Learning -- Evaluation Criteria and Model Selection -- Machine Learning Algorithms -- Applications of Machine Learning: Signal/Image Processing -- Applications of Machine Learning: Energy Systems -- Applications of Machine Learning: Robotics -- State of the Art of Machine Learning.
Contained By:	Springer Nature eBook
Subject:	Machine Learning. -
Online resource:	https://doi.org/10.1007/978-3-031-46990-9
ISBN:	9783031469909

Machine learning crash course for engineers
Hossain, Eklas.

Machine learning crash course for engineers[electronic resource] /by Eklas Hossain. - Cham :Springer International Publishing :2024. - xx, 453 p. :ill. (chiefly col.), digital ;24 cm.

Introduction to Machine Learning -- Evaluation Criteria and Model Selection -- Machine Learning Algorithms -- Applications of Machine Learning: Signal/Image Processing -- Applications of Machine Learning: Energy Systems -- Applications of Machine Learning: Robotics -- State of the Art of Machine Learning.

Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly. A concise guide to the basics of algorithms, building models, and performance evaluation; Offers highly illustrated, step-by-step guidelines with Python programming examples; Provides examples and exercises related to signal and image processing, energy systems, and robotics.

ISBN: 9783031469909

Standard No.: 10.1007/978-3-031-46990-9doiSubjects--Topical Terms:

3382522
Machine Learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning crash course for engineers
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245 1 0 $a Machine learning crash course for engineers $h [electronic resource] / $c by Eklas Hossain.
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300 $a xx, 453 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a Introduction to Machine Learning -- Evaluation Criteria and Model Selection -- Machine Learning Algorithms -- Applications of Machine Learning: Signal/Image Processing -- Applications of Machine Learning: Energy Systems -- Applications of Machine Learning: Robotics -- State of the Art of Machine Learning.
520 $a Machine Learning Crash Course for Engineers is a reader-friendly introductory guide to machine learning algorithms and techniques for students, engineers, and other busy technical professionals. The book focuses on the application aspects of machine learning, progressing from the basics to advanced topics systematically from theory to applications and worked-out Python programming examples. It offers highly illustrated, step-by-step demonstrations that allow readers to implement machine learning models to solve real-world problems. This powerful tutorial is an excellent resource for those who need to acquire a solid foundational understanding of machine learning quickly. A concise guide to the basics of algorithms, building models, and performance evaluation; Offers highly illustrated, step-by-step guidelines with Python programming examples; Provides examples and exercises related to signal and image processing, energy systems, and robotics.
650 0 $a Machine Learning. $3 3382522
650 2 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Electrical and Electronic Engineering. $3 3591890
650 2 4 $a Signal, Speech and Image Processing. $3 3592727
650 2 4 $a Electrical Power Engineering. $3 3592498
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