Practical Machine Learning in R

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).