The book Practical Machine Learning with R provides a section "Digital Resources" at the end of each chapter. The provided resources include videos, tutorials, and R scripts that are related to the chapter.

Below you find links to these "Digital Resources":

Chapter 2: Introduction to Machine Learning

Chapter 4: k-Nearest Neighbors - Getting Started

Chapter 5: Linear Regression - Key Machine Learning Concepts

Chapter 6: Polynomial Regression - Overfitting & Tuning Explained

Chater 7: Ridge, Lasso, and Elastic Net - Regularization Explained

Chapter 8: Logistic Regression - Handling Imbalanced Data

Chapter 9: Deep Learning - MLP Neural Networks Explained

Chapter 10: Tree-Based Models/Decision Trees

Chapter 10: Tree-Based Models/Random Forest

Chapter 10: Tree-Based Models/Boosting Trees Algorithms

Chapter 11: Interpretation of Machine Learning Results