000 03948cam a22005775i 4500
001 21772749
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005 20250426155915.0
006 m |o d |
007 cr |||||||||||
008 160316s2016 gw |||| o |||| 0|eng
010 _a 2019762086
020 _a9783319307176
024 7 _a10.1007/978-3-319-30717-6
_2doi
035 _a(DE-He213)978-3-319-30717-6
040 _aDLC
_beng
_epn
_erda
_cJKRC
072 7 _aTEC041000
_2bisacsh
072 7 _aTJK
_2bicssc
072 7 _aTJK
_2thema
082 0 4 _a621.382
_223
_bUNP.J
100 1 _aUnpingco, Jose.
_eAuthor
_933933
245 1 0 _aPython for probability, statistics and machine learning /
_cby Jose Unpingco.
_hEnglish.
250 _a3rd.
260 _aSwitzerland :
_bSpringer,
_c2022.
264 1 _b :
300 _axvii, 509 p.
_c24 cm.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
365 _b89.99
_cEuro
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.
588 _aDescription based on publisher-supplied MARC data.
650 0 _aApplied mathematics.
_92749
650 0 _aData mining.
650 0 _aElectrical engineering.
_932721
650 0 _aEngineering mathematics.
_92751
650 0 _aMathematical statistics.
650 0 _aStatistics.
650 1 4 _aCommunications Engineering, Networks.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T24035
_933934
650 2 4 _aData Mining and Knowledge Discovery.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I18030
_933935
650 2 4 _aMathematical and Computational Engineering.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T11006
_92760
650 2 4 _aProbability and Statistics in Computer Science.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I17036
_933936
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_0https://scigraph.springernature.com/ontologies/product-market-codes/S17020
_930258
776 0 8 _iPrint version:
_tPython for probability, statistics, and machine learning.
_z9783319307152
_w(DLC) 2016933108
776 0 8 _iPrinted edition:
_z9783319307152
776 0 8 _iPrinted edition:
_z9783319307169
906 _a0
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