Random Forests

This course provides a comprehensive overview of Random Forests, an ensemble learning technique for classification and regression problems. Through the combination of theory and hands-on instruction, students will receive an expertise on how to build, use and evaluate Random Forests. Part 1 will cover topics such as how to identify cases, prepare features and select a model. Part 2 explores missing data, how to cluster observations and use techniques such as boosting and bagging. Lastly, Part 3 gives an introduction on how to use the Random Forests in R programming language. Students will gain a greater understanding of the modern machine learning methods and its application in practice.