Hyperparameter Tuning Techniques

The process of training a machine learning (ML) model is all about learning the right values for model parameters based on the training data. Equally important, but less often studied, is the process of hyperparameter tuning, which involves settling on the optimal hyperparameter values, which do not change during the training process.In this course, learn the differences between model parameters and hyperparameters and how grid search tunes hyperparameters for better model performance. Next, discover how cross-validation helps improve the hyperparameter tuning process and how singular and k-fold cross-validation work. Finally, compare and contrast several different hyperparameter tuning approaches.After completing this course, you will be able to outline hyperparameter tuning techniques.