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001			85754
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008			160316s2016 gw | s |||| 0|eng d
020			_a9783319307176
040			_aES-MaUEC
050		4	_aQA76.73.P98 _bU575 2016
082	0	4	_a621.382
100	1		_aUnpingco, José. _998936 _0Local
245	1	0	_aPython for Probability, Statistics, and Machine Learning _cby José Unpingco
250			_a1st ed.
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XV, 276 páginas) _b110 ilustraciones, 7 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
505	0		_aGetting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.
520	3		_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.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, asignarmaterias , EBSPRINGER
650		7	_aEstadística matemática _0comprobar BNE19900966258 _2embne _9138936
650		7	_aProbabilidades _xData processing _0(OCoLC)1077741 _2embne _0 _9405075
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-30717-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319307176
907			_a.b12950324 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aQA76.73.P98 U575 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11593441 _z06-04-17
999			_c85754 _d85754 _x1