000 03442nam a22005415i 4500
001 978-3-319-30717-6
003 DE-He213
005 20200421112555.0
007 cr nn 008mamaa
008 160316s2016 gw | s |||| 0|eng d
020 _a9783319307176
_9978-3-319-30717-6
024 7 _a10.1007/978-3-319-30717-6
_2doi
050 4 _aTK1-9971
072 7 _aTJK
_2bicssc
072 7 _aTEC041000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aUnpingco, Jos�e.
_eauthor.
245 1 0 _aPython for Probability, Statistics, and Machine Learning
_h[electronic resource] /
_cby Jos�e 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 _aEngineering.
650 0 _aMathematical statistics.
650 0 _aData mining.
650 0 _aStatistics.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 0 _aElectrical engineering.
650 1 4 _aEngineering.
650 2 4 _aCommunications Engineering, Networks.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aProbability and Statistics in Computer Science.
650 2 4 _aData Mining and Knowledge Discovery.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319307152
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-30717-6
912 _aZDB-2-ENG
942 _cEBK
999 _c59117
_d59117