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082 0 4 _a006.31
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049 _aMAIN
100 1 _aBowles, Michael,
_eauthor.
245 1 0 _aMachine learning in Python :
_bessential techniques for predictive analysis /
_cMichael Bowles.
264 1 _aIndianapolis, IN :
_bWiley,
_c[2015]
264 4 _c�2015
300 _a1 online resource :
_bcolor illustrations
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aThe Two Essential Algorithms for Making Predictions -- Understand the Problem by Understanding the Data -- Predictive Model Building: Balancing Performance, Complexity, and Big Data -- Penalized Linear Regression -- Building Predictive Models Using Penalized Linear Methods -- Ensemble Methods -- Building Ensemble Models with Python.
588 0 _aOnline resource; title from PDF title page (Ebsco, viewed April 13, 2015).
504 _aIncludes bibliographical references and index.
520 8 _aLearn a simpler and more effective way to analyze data and predict outcomes with Python 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. Predict outcomes using linear and ensemble algorithm families Build predictive models that solve a range of simple and complex problems Apply core machine learning algorithms using Python Use sample code directly to build custom solutions Machine learning doesn't have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python.
520 8 _aShows you how to do this, without requiring an extensive background in math or statistics.
590 _aJohn Wiley and Sons
_bWiley Online Library: Complete oBooks
650 0 _aMachine learning.
650 0 _aPython (Computer program language)
650 6 _aApprentissage automatique.
650 6 _aPython (Langage de programmation)
650 7 _aCOMPUTERS
_xGeneral.
_2bisacsh
650 7 _aMachine learning
_2fast
650 7 _aPython (Computer program language)
_2fast
655 7 _adissertations.
_2aat
655 7 _aAcademic theses
_2fast
655 7 _aAcademic theses.
_2lcgft
655 7 _aTh�eses et �ecrits acad�emiques.
_2rvmgf
758 _ihas work:
_aMachine Learning in Python (Text)
_1https://id.oclc.org/worldcat/entity/E39PCFv8FTh6f9g8qtHHG7ckQm
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856 4 0 _uhttps://onlinelibrary.wiley.com/doi/book/10.1002/9781119183600
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