This chapter introduces advanced machine learning (ML) methods, using simulated datasets in R to demonstrate their application in public health research. Readers are guided through support vector machines (SVM), ensemble methods such as gradient boosting, and deep learning, with comparisons across models in terms of accuracy, interpretability, scalability, and computational demand. Special attention is given to the concept of the decision boundary in SVM, showing how models separate different classes and why this is important for classification tasks in health data. The chapter also highlights natural language processing (NLP) as a powerful approach for analyzing unstructured text, including clinical notes and survey responses. By working through these examples, learners gain both conceptual understanding and practical skills for evaluating and applying advanced ML techniques to diverse public health challenges, from disease prediction to population risk assessment.