Practical Machine Learning in R

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Practical Machine Learning in R

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內容描述

R Programming for Machine Learning shows readers machine learning with a hands on approach to the practical algorithms and applications to solve business problems with machine learning. The book begins by explaining machine learning and its organizational benefits, moves to hands on data management including dimensionality reduction, and then introduces R and the popular RStudio tool. In Unsupervised Learning the reader works with patterns including apriori and eclat and grouping data with clustering (k-means and hierarchical). From there, R Programming for Machine Learning covers the crucial classification techniques Nearest Neighbor, Decision Trees, and Naive Bayes. The regression techniques are then covered before performance evaluation including choosing the right model and ensemble methods (Random Forest, XGBoost).

作者介紹

FRED NWANGANGA, PHD, is an assistant teaching professor of business analytics at the University of Notre Dame's Mendoza College of Business. He has over 15 years of technology leadership experience. MIKE CHAPPLE, PHD, is associate teaching professor of information technology, analytics, and operations at the Mendoza College of Business. Mike is a bestselling author of over 25 books, and he currently serves as academic director of the University's Master of Science in Business Analytics program.