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020 _a9783319307176
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024 7 _a10.1007/978-3-319-30717-6
_2doi
050 4 _aTK5101-5105.9
072 7 _aTJK
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072 7 _aTEC041000
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082 0 4 _a621.382
_223
100 1 _aUnpingco, José.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_963433
245 1 0 _aPython for Probability, Statistics, and Machine Learning
_h[electronic resource] /
_cby José Unpingco.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXV, 276 p. 110 illus., 7 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 _aGetting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.
520 _aThis book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowledge of Python programming. Explains how to simulate, conceptualize, and visualize random statistical processes and apply machine learning methods; Connects to key open-source Python communities and corresponding modules focused on the latest developments in this area; Outlines probability, statistics, and machine learning concepts using an intuitive visual approach, backed up with corresponding visualization codes.
650 0 _aTelecommunication.
_910437
650 0 _aEngineering mathematics.
_93254
650 0 _aEngineering—Data processing.
_931556
650 0 _aStatistics .
_931616
650 0 _aComputer science—Mathematics.
_931682
650 0 _aMathematical statistics.
_99597
650 0 _aData mining.
_93907
650 1 4 _aCommunications Engineering, Networks.
_931570
650 2 4 _aMathematical and Computational Engineering Applications.
_931559
650 2 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_931790
650 2 4 _aProbability and Statistics in Computer Science.
_931857
650 2 4 _aData Mining and Knowledge Discovery.
_963434
710 2 _aSpringerLink (Online service)
_963435
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319307152
776 0 8 _iPrinted edition:
_z9783319307169
856 4 0 _uhttps://doi.org/10.1007/978-3-319-30717-6
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
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