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008			210311s2021 gw a o |||| 0|eng d
020			_a9783030600327
024	7		_a10.1007/978-3-030-60032-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ335 _b2021 EB
100	1		_aSomogyi, Zoltán _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9678658
245	1	4	_aThe application of artificial intelligence : _bstep-by-step guide from beginner to expert _cby Zoltán Somogyi
250			_aFirst edition 2021
264		1	_aCham, Switzerland _bSpringer International Publising _c2021
300			_a1 recurso en línea (XXXV, 431 páginas) _b303 ilustraciones, 228 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF _2rda
505	0		_aPart I, Introduction -- An Introduction to Machine Learning and Artificial Intelligence (AI) -- Part II, An In-Depth Overview of Machine Learning -- Machine Learning Algorithms -- Performance Evaluation of Machine Learning Models -- Machine Learning Data -- Part III, Automatic Speech Recognition -- Automatic Speech Recognition -- Part IV, Biometrics Recognition -- Face Recognition -- Speaker Recognition -- Part V, Machine Learning by Example -- Machine Learning by Example -- Part VI, The AI-Toolkit: Machine Learning Made Simple -- The AI-Toolkit: Machine Learning Made Simple -- App. A, From Regular Expressions to HMM -- References -- Index.
520	3		_aThis book presents a unique, understandable view of machine learning using many practical examples and access to free professional software and open source code. The user-friendly software can immediately be used to apply everything you learn in the book without the need for programming. After an introduction to machine learning and artificial intelligence, the chapters in Part II present deeper explanations of machine learning algorithms, performance evaluation of machine learning models, and how to consider data in machine learning environments. In Part III the author explains automatic speech recognition, and in Part IV biometrics recognition, face- and speaker-recognition. By Part V the author can then explain machine learning by example, he offers cases from real-world applications, problems, and techniques, such as anomaly detection and root cause analyses, business process improvement, detecting and predicting diseases, recommendation AI, several engineering applications, predictive maintenance, automatically classifying datasets, dimensionality reduction, and image recognition. Finally, in Part VI he offers a detailed explanation of the AI-TOOLKIT, software he developed that allows the reader to test and study the examples in the book and the application of machine learning in professional environments. The author introduces core machine learning concepts and supports these with practical examples of their use, so professionals will appreciate his approach and use the book for self-study. It will also be useful as a supplementary resource for advanced undergraduate and graduate courses on machine learning and artificial intelligence.
988			_aSpringer_Computer_2021
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _aAprendizaje automático _9166090
710	2		_aSpringerLink
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-60032-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2021 _dz _eb _zSI