ML Parameters Optimization: GridSearch, Bayesian, Random

About this course

Hello everyone and welcome to this new hands-on project on Machine Learning hyperparameters optimization. In this project, we will optimize machine learning regression models parameters using several techniques such as grid search, random search and Bayesian optimization. Hyperparameter optimization is a key step in developing machine learning models and it works by fine tuning ML models so they can optimally perform on a given dataset.

What you will learn

• Understand the difference between hyperparameters optimization techniques such as GridSearch, Bayesian & Random Search Optimization Techniques.
• Optimize ML model hyperparameters in Scikit-Learn using GridSearch, Bayesian & Random Search Optimization Techniques.
• Evaluate several trained regression models performance using various Key Performance Indicators (KPIs).