02992cam a2200433Ii 4500999001700000001001300017003000600030005001700036008004100053010001700094015001900111016001800130020003100148020002800179020003200207020002700239029002200266029002100288035003900309037010700348040012600455049000900581050002200590055002500612082001500637100003000652245009800682264005600780264000900836300004600845336002600891337002800917338002700945520134700972650004802319650006502367942001402432952011202446 c59502d59502ocn889736073OCoLC20200207144856.0140827t20152015inua b 001 0 eng d a 2015930541 aGBB5131172bnb7 a0170370892Uk a9781118961742q(paperback) a1118961749q(paperback) z9781118961759q(ePub ebook) z9781118961766q(ebook)1 aAU@b0000548341291 aUKMGBb017037089 a(OCoLC)889736073z(OCoLC)909245561 bJohn Wiley & Sons Inc, Order Processing Dept 432 Elizabeth Ave, Somerset, NJ, USA, 08873nSAN 200-2272 aBTCTAbengcBTCTAdBDXdJRZdYDXCPdCDXdOCLCFdNYPdFJDdOCLCQdVRCdI8MdFIEdSFRdOCLCQdUKMGBdDLCdCNMTRdOCLCQdCNR aCNRM 4aQ325.5b.B69 2015 3aQ325.5b.B695 2015eb04a006.312231 aBowles, Michael,eauthor.10aMachine Learning in Python :bessential techniques for predictive analysis /cMichael Bowles. 1aIndianapolis, IN :bJohn Wiley & Sons, Inc.,c2015. 4c2015 axxix, 326 pages :billustrations ;c24 cm atextbtxt2rdacontent aunmediatedbn2rdamedia avolumebnc2rdacarrier a'Machine Learning in Python' shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions. Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.--cProvided by publisher. 7aMachine learning.2fast0(OCoLC)fst01004795 7aPython (Computer program language)2fast0(OCoLC)fst01084736 2lcccBOOK 00102lcc4070aNCARbNCARcMLd2020-02-07oQ325.5 .B69 2015p50583020009084r2020-02-07w2020-02-07yBOOK