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001 978-3-030-60032-7
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020 _a9783030600327
_9978-3-030-60032-7
024 7 _a10.1007/978-3-030-60032-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aSomogyi, Zoltán.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 4 _aThe Application of Artificial Intelligence
_h[electronic resource] :
_bStep-by-Step Guide from Beginner to Expert /
_cby Zoltán Somogyi.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXXXV, 431 p. 303 illus., 228 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
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 _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.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 0 _aArtificial intelligence
_xData processing.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
650 2 4 _aData Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030600310
776 0 8 _iPrinted edition:
_z9783030600334
776 0 8 _iPrinted edition:
_z9783030600341
856 4 0 _uhttps://doi.org/10.1007/978-3-030-60032-7
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c174103
_d174103