ML Parameters Optimization: GridSearch, Bayesian, Random

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ML Parameters Optimization: GridSearch, Bayesian, Random provided by Coursera is a comprehensive online course, which lasts for 2 hours worth of material. ML Parameters Optimization: GridSearch, Bayesian, Random is taught by Ryan Ahmed. Upon completion of the course, you can receive an e-certificate from Coursera. The course is taught in Englishand is Paid Course. Visit the course page at Coursera for detailed price information.

Overview
  • 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.