Machine Learning in R (Hardcover)

Introduction to the Book. Basics of R. Introduction to Machine Learning With a Bit of History. Partitioning Methods and Trees. Regression and Classification Trees (Rpart, Tree Packages). Random Forests (Random Forests and Ipred, Packages). Support Vector Machines and Kernel Methods. SVM’s (Package Kernlab, e1071). KPCA (Package Kernlab). Spectral Clustering (Kernlab). Boosting. Generalized Boosting Models (Classification, Regression, Ranking In Package GBM). Neural Networks and Deep Learning. Feed-Forward Networks (Package Nnet). Restricted Boltzmann Machines. Deep Belief Networks. Autoencoders (Stacked). Multilayer Feed-Forward Networks (Package Deepnet, Darch, H20). Bayesian Methods. Naive Bayes (Package e1071). Topic Modeling. Latent Dirichlet Allocation (Package Topic Modeling). Clustering. Hierarchical Clustering. Partitioning Clustering. Model-Based Clustering. Model Selection and Evaluation. Tune Parameters (Function Tune In e1071). Evaluating Models (Package ROCR, etc.).