000			02736nmm a2200205Ia 4500
008			220920s9999||||xx |||||||||||||| ||und||
020			_a9781788295864
082			_a005.8 _bL296M
100			_aLantz, Brett _eAuthor _lEnglish _9874
245		0	_aMachine Learning with R _b: Expert Techniques for Predictive Modeling _c/ by Brett Lantz. _h[Electronic Resource]
250			_a3rd Ed.
260			_aBirmingham, UK _b: Packt Publishing, _c2019
300			_a375p.
520			_aSolve real-world data problems with R and machine learningKey FeaturesThird edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyondHarness the power of R to build flexible, effective, and transparent machine learning modelsLearn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett LantzBook DescriptionMachine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data.Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings.This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R.What you will learnDiscover the origins of machine learning and how exactly a computer learns by examplePrepare your data for machine learning work with the R programming languageClassify important outcomes using nearest neighbor and Bayesian methodsPredict future events using decision trees, rules, and support vector machinesForecast numeric data and estimate financial values using regression methodsModel complex processes with artificial neural networks - the basis of deep learningAvoid bias in machine learning modelsEvaluate your models and improve their performanceConnect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlowWho this book is forData scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.
650			_aMachine Learning _915487
650			_aR _915488
650			_aStatistics- Data Processing _9875
856			_uhttp://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2106304 _qPDF _yClick to Access the Online Book
942			_cEBK _nYes
999			_c11966 _d11966