In many machine learning scenarios, it is insufficient to simply run a model and obtain a prediction. The goal is to obtain the best model that provides the most accurate predictions. One effective approach to achieve this is through hyperparameter tuning, which involves optimizing the specific settings for a given model. This course focuses on utilizing the caret, mlr, and h2o packages to efficiently identify the optimal combination of hyperparameters. Various techniques such as grid search, random search, adaptive resampling, and automatic machine learning (AutoML) will be explored. Additionally, participants will have the opportunity to work with diverse datasets and fine-tune different supervised learning models, including random forests, gradient boosting machines, support vector machines, and neural networks. Prepare yourself for an immersive experience in the art of model tuning!