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010   $a3-319-30717-7
024 7 $a10.1007/978-3-319-30717-6
035   $a(CKB)3710000000616313
035   $a(EBL)4453022
035   $a(OCoLC)945095092
035   $a(SSID)ssj0001653795
035   $a(PQKBManifestationID)16433748
035   $a(PQKBTitleCode)TC0001653795
035   $a(PQKBWorkID)14982671
035   $a(PQKB)11585096
035   $a(DE-He213)978-3-319-30717-6
035   $a(MiAaPQ)EBC4453022
035   $a(PPN)19277462X
035   $a(EXLCZ)993710000000616313
100   $a20160316d2016      u|         0
101 0 $aeng
135   $aur|n|---|||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aPython for probability, statistics, and machine learning /$fby José Unpingco
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource
300   $aDescription based upon print version of record.
311   $a3-319-30715-0 
320   $aIncludes bibliographical references and index.
327   $aGetting Started with Scientific Python -- Probability -- Statistics -- Machine Learning -- Notation.
330   $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.
606   $aElectrical engineering
606   $aApplied mathematics
606   $aEngineering mathematics
606   $aStatistics
606   $aMathematical statistics
606   $aData mining
606   $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035
606   $aMathematical and Computational Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T11006
606   $aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17020
606   $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
615  0$aElectrical engineering.
615  0$aApplied mathematics.
615  0$aEngineering mathematics.
615  0$aStatistics.
615  0$aMathematical statistics.
615  0$aData mining.
615 14$aCommunications Engineering, Networks.
615 24$aMathematical and Computational Engineering.
615 24$aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
615 24$aProbability and Statistics in Computer Science.
615 24$aData Mining and Knowledge Discovery.
676   $a620
700   $aUnpingco$b José$4aut$4http://id.loc.gov/vocabulary/relators/aut$0762830
906   $aBOOK
912   $a9910253968503321
996   $aPython for Probability, Statistics, and Machine Learning$91547068
997   $aUNINA