000			02049 a2200217 4500
999			_c1589 _d1589
003			OSt
005			20190207140422.0
008			190207b ||||| |||| 00| 0 eng d
020			_a9781784393908
040			_cIIMV
082			_a006.31
100	1		_aBrett Lantz _eAuthor _9877
245	1		_aMachine learning with R _bDiscover how to build machine learning algorithms, prepare data,and dig deep into data prediction techniques with R _cby Brett Lantz
250			_a2
260			_bPackt _c2015 _aMumbai
300			_axiii, 426 pages: _bIlustrations; _c24 cm.
505	0		_a1 Introducing Machine Learning 2 Managing and Understanding Data 3 Lazy Learning – Classification Using Nearest Neighbors 4 Probabilistic Learning – Classification Using Naive Bayes 5 Divide and Conquer – Classification Using Decision Trees and Rules 6 Forecasting Numeric Data – Regression Methods 7 Black Box Methods – Neural Networks and Support Vector Machines 8 Finding Patterns – Market Basket Analysis Using Association Rules 9 Finding Groups of Data – Clustering with k-means 10 Evaluating Model Performance 11 Improving Model Performance 12 Specialized Machine Lea
520	3		_aUpdated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.
650		0	_2Computer Science
942			_2ddc _cBK