ML Parameters Optimization: GridSearch, Bayesian, Random
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ML Parameters Optimization: GridSearch, Bayesian, Random

Highlights

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.

About the Course Provider

Coursera provides access to more than 3000+ courses across a wide variety of subjects in parntership with different universities and organizations.

Course by

  • self
    Self paced
  • dueration
    Duration 3 hours
  • domain
    Domain Personal Development
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  • language
    Language English