000			02305cam a22002897a 4500
999			_c11274 _d11274
001			20673517
005			20190411104427.0
008			180918t20182018caua 001 0 eng d
010			_a 2018303264
020			_a9789352137251
035			_a(OCoLC)on1031406477
040			_aSXP _beng _cSXP _dSXP _dJRZ _dOQX _dEYM _dOCLCF _dDLC
042			_alccopycat
050	0	0	_aQ325.5 _b.B85 2018
082	0	4	_a006.31 _223 _bBUR
100	1		_aBurger, Scott V., _eauthor.
245	1	0	_aIntroduction to machine learning with R : _brigorous mathematical analysis / _cScott V. Burger.
260			_aMumbai : _bShroff Publishers& Distributors, _c2018.
300			_aix, 212 p. : _bill. ; _c24 cm
500			_aIncludes index.
505	0		_aWhat is a model? -- Supervised and unsupervised machine learning -- Sampling statistics and model training in R -- Regression in a nutshell -- Neural networks in a nutshell -- Tree-based methods -- Other advanced methods -- Machine learning with the caret package -- Encyclopedia of machine learning models in caret.
520			_aMachine learning can be a difficult subject if you're not familiar with the basics. With this book, you'll get a solid foundation of introductory principles used in machine learning with the statistical programming language R. You'll start with the basics like regression, then move into more advanced topics like neural networks, and finally delve into the frontier of machine learning in the R world with packages like Caret. By developing a familiarity with topics like understanding the difference between regression and classification models, you'll be able to solve an array of machine learning problems. Knowing when to use a specific model or not can mean the difference between a highly accurate model and a completely useless one. This book provides copious examples to build a working knowledge of machine learning. Understand the major parts of machine learning algorithms Recognize how machine learning can be used to solve a problem in a simple manner Figure out when to use certain machine learning algorithms versus others Learn how to operationalize algorithms with cutting edge packages
650		0	_aMachine learning.
650		0	_aR (Computer program language)
650		0	_aStatistics _xData processing.
942			_2ddc _cBK