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020 _a9783031437250
_9978-3-031-43725-0
024 7 _a10.1007/978-3-031-43725-0
_2doi
050 4 _aTK7867-7867.5
072 7 _aTJFC
_2bicssc
072 7 _aTEC008010
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072 7 _aTJFC
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100 1 _aGupta, Pramod.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_970988
245 1 0 _aEssentials of Python for Artificial Intelligence and Machine Learning
_h[electronic resource] /
_cby Pramod Gupta, Anupam Bagchi.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIX, 509 p. 163 illus., 148 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Engineering, Science, and Technology,
_x2690-0327
505 0 _aIntroduction -- Statistical Methods and Models -- Python Language Basics -- Introduction to Numpy -- Introduction to Pandas -- Data Manipulation With Pandas -- Data Visualization With Python -- Machine Learning -- Data Pipelines Using Python -- Mlops: Machine Learning Operations.
520 _aThis book introduces the essentials of Python for the emerging fields of Machine Learning (ML) and Artificial Intelligence (AI). The authors explore the use of Python’s advanced module features and apply them in probability, statistical testing, signal processing, financial forecasting, and various other applications. This includes mathematical operations with array data structures, Data Manipulation, Data Cleaning, machine learning, Data pipeline, probability density functions, interpolation, visualization, and other high-performance benefits using the core scientific packages NumPy, Pandas, SciPy, Sklearn/Scikit learn and Matplotlib. Readers will gain a deep understanding with problem-solving experience on these powerful platforms when dealing with engineering and scientific problems related to Machine Learning and Artificial Intelligence. Several examples of real problems using these techniques are provided along with examples. The authors also focus on the best practices in the industry on using Python for AI and ML. Deployment on a cloud infrastructure is described in detail (with code) to emphasize real scenarios. Includes several real examples of how to write and deploy code, including on a cloud infrastructure Provides single-source on Python for machine learning and artificial intelligence, from basics to real implementation Includes sufficient coverage of Python libraries, frameworks, and tools to develop complex data science applications.
650 0 _aElectronic circuits.
650 0 _aMachine learning.
_955059
650 0 _aSignal processing.
650 1 4 _aElectronic Circuits and Systems.
650 2 4 _aMachine Learning.
_955059
650 2 4 _aSignal, Speech and Image Processing.
700 1 _aBagchi, Anupam.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_970989
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031437243
776 0 8 _iPrinted edition:
_z9783031437267
776 0 8 _iPrinted edition:
_z9783031437274
830 0 _aSynthesis Lectures on Engineering, Science, and Technology,
_x2690-0327
856 4 0 _uhttps://doi.org/10.1007/978-3-031-43725-0
912 _aZDB-2-SXSC
999 _c57620
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