Machine Learning with caret in R

Regression Models: Fitting and Evaluating Their Performance
-In the first chapter of this course, you'll fit regression models with train() and evaluate their out-of-sample performance using cross-validation and root-mean-square error (RMSE).

Classification Models: Fitting and Evaluating Their Performance
-In this chapter, you'll fit classification models with train() and evaluate their out-of-sample performance using cross-validation and area under the curve (AUC).

Tuning Model Parameters to Improve Performance
-In this chapter, you will use the train() function to tweak model parameters through cross-validation and grid search.

Preprocessing Data
-In this chapter, you will practice using train() to preprocess data before fitting models, improving your ability to making accurate predictions.

Selecting Models: A Case Study in Churn Prediction
-In the final chapter of this course, you'll learn how to use resamples() to compare multiple models and select (or ensemble) the best one(s).