000			01997nam a22002057a 4500
999			_c65269 _d65269
008			200605b2018 ||||| |||| 00| 0 eng d
020			_a9789352137251
082			_a006.31 BUR-S
100			_aBurger, Scott V.
245			_aIntroduction to machine learning with R : _brigorous mathematical analysis / _cScott V. Burger
260			_aIndia _bShorff Publishers & Distributors _c2018
300			_a212 p.
365			_aINR _b650.00.
500			_aMachine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods. Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you’ll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning. Explore machine learning models, algorithms, and data training Understand machine learning algorithms for supervised and unsupervised cases Examine statistical concepts for designing data for use in models Dive into linear regression models used in business and science Use single-layer and multilayer neural networks for calculating outcomes Look at how tree-based models work, including popular decision trees Get a comprehensive view of the machine learning ecosystem in R Explore the powerhouse of tools available in R’s caret package
650			_aR (Computer program language)
650			_aMachine learning
650			_aStatistics--Data processing
650			_aMathematical statistics--Data processing
650			_aNeural networks (Computer science)