ML Parameters Optimization: GridSearch, Bayesian, Random
$9.99
ENROLL NOWCourse Overview
What You'll Learn
- 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.
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.
Course FAQs
Is this an accredited online course?
Accreditation for 'ML Parameters Optimization: GridSearch, Bayesian, Random' is determined by the provider, Coursera Project Network. For online college courses or degree programs, we strongly recommend you verify the accreditation status directly on the provider's website to ensure it meets your requirements.
Can this course be used for continuing education credits?
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How do I enroll in this online school program?
To enroll, click the 'ENROLL NOW' button on this page. You will be taken to the official page for 'ML Parameters Optimization: GridSearch, Bayesian, Random' on the Coursera Project Network online class platform, where you can complete your registration.
