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001 | 978-3-031-12282-8 | ||
003 | DE-He213 | ||
005 | 20240423124951.0 | ||
007 | cr nn 008mamaa | ||
008 | 221216s2023 sz | s |||| 0|eng d | ||
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_a9783031122828 _9978-3-031-12282-8 |
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024 | 7 |
_a10.1007/978-3-031-12282-8 _2doi |
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_a621,382 _223 |
100 | 1 |
_aJoshi, Ameet V. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aMachine Learning and Artificial Intelligence _h[electronic resource] / _cby Ameet V Joshi. |
250 | _a2nd ed. 2023. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2023. |
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300 |
_aXXI, 271 p. 129 illus., 125 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _aIntroduction -- Introduction to AI and ML -- Essential Concepts in Artificial Intelligence and Machine Learning -- Data Understanding, Representation, and Visualization -- Linear Methods -- Perceptron and Neural Networks -- Decision Trees -- Support Vector Machines -- Probabilistic Models -- Dynamic Programming and Reinforcement Learning -- Evolutionary Algorithms -- Time Series Models -- Deep Learning -- Emerging Trends in Machine Learning -- Unsupervised Learning -- Featurization -- Designing and Tuning -- Model Pipelines -- Performance Measurement -- Classification -- Regression -- Ranking -- Recommendations Systems -- Azure Machine Learning -- Open Source Machine Learning Libraries -- Amazon’s Machine Learning Toolkit: Sagemaker -- Conclusion. | |
520 | _aThe new edition of this popular professional book on artificial intelligence (ML) and machine learning (ML) has been revised for classroom or training use. The new edition provides comprehensive coverage of combined AI and ML theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The fourth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems. Each chapter is accompanied with a set of exercises that will help the reader / student to apply the learnings from the chapter to a real-life problem. Completion of these exercises will help the reader / student to solidify the concepts learned. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible. The book covers a large gamut of topics in the area of AI and ML and a professor can tailor a course on AI / ML based on the book by selecting and re-organizing the sequence of chapters to suit the needs. | ||
650 | 0 | _aTelecommunication. | |
650 | 0 | _aMachine learning. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aCommunications Engineering, Networks. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aComputational Intelligence. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031122811 |
776 | 0 | 8 |
_iPrinted edition: _z9783031122835 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-12282-8 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c172734 _d172734 |