000			02352nam a22003015i 4500
001			401873
003			DE-He213
005			20240514120029.0
007			cr nn 008mamaa
008			231226s2024 sz | o |||| 0|eng d
020			_a9783031469909
024	7		_a10.1007/978-3-031-46990-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5-.7 _b2024 EB
100	1		_aHossain, Eklas. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aMachine Learning Crash Course for Engineers _cby Eklas Hossain
250			_a1st ed. 2024.
264		1	_aCham _bSpringer International Publishing _c2024
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aIntroduction 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			_aMachine 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.
942			_2lcc _cLE
988			_aSpringer_Engineering_2024
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-46990-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
999			_c401873 _d401873