Machine Learning in R: a Hands-on Experience 0.1. Teaser Trailer Session 1 1.1. Introduction to the Tidyverse 1.2. Machine Learning Libraries in R 1.3. K NEarest Neighbors 1.4. Support Vector Machines 1.5. Tree-based Models 1.6. Titanic Dataset Session 2 2.1. Bias-Variance Tradeoff 2.2. Model Performance 2.3. Model Selection 2.4. Hyperparameter Optimization 2.5. Credit Card Fraud Dataset Session 3 3.1. Introduction to Neural Networks 3.2. Convolutional Neural Networks 3.3. Recurrent Neural Networks 3.4. Regularization Techniques 3.5. Convnets for Text Recognition
