Machine Learning Crash Course for Engineers
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...
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| Autor principal: | |
|---|---|
| Autor Corporativo: | |
| Formato: | Recurso Eletrônico livro eletrônico |
| Idioma: | inglês |
| Publicado em: |
Cham :
Springer International Publishing : Imprint: Springer,
2024.
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| Edição: | 1st ed. 2024. |
| Assuntos: | |
| Acesso em linha: | https://doi.org/10.1007/978-3-031-46990-9 |
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| 001 | 978-3-031-46990-9 | ||
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| 020 | |a 9783031469909 |9 978-3-031-46990-9 | ||
| 024 | 7 | |a 10.1007/978-3-031-46990-9 |2 doi | |
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| 100 | 1 | |a Hossain, Eklas. |e author. |0 (orcid)0000-0003-2332-8095 |1 https://orcid.org/0000-0003-2332-8095 |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Machine Learning Crash Course for Engineers |h [electronic resource] / |c by Eklas Hossain. |
| 250 | |a 1st ed. 2024. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2024. | |
| 300 | |a 1 online resource (XX, 453 p. 170 illus.). |b 156 illus. in color. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 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. | ||
| 588 | |a Print version record. | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Electrical engineering. | |
| 650 | 0 | |a Signal processing. | |
| 650 | 0 | |a Electric power production. | |
| 650 | 1 | 4 | |a Machine Learning. |
| 650 | 2 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Electrical and Electronic Engineering. |
| 650 | 2 | 4 | |a Signal, Speech and Image Processing. |
| 650 | 2 | 4 | |a Electrical Power Engineering. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783031469893 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031469916 |
| 776 | 0 | 8 | |i Printed edition: |z 9783031469923 |
| 856 | 4 | 0 | |u https://doi.org/10.1007/978-3-031-46990-9 |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||