ML Parameters Optimization: GridSearch, Bayesian, Random
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.
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- Level Beginner
- Duration 3 hours
- Course by Coursera Project Network
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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.Modules
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- Assess Your Knowledge
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- ML Parameters Optimization: GridSearch, Bayesian, Random
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- Project Overview
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Discover the essential techniques for optimizing machine learning regression models in this hands-on course. Led by Coursera, you'll explore grid search, random search, and Bayesian optimization methods. Perfect for beginners, this 180-minute course focuses on fine-tuning ML models for optimal performance. Available for free, it's an ideal choice for those looking to enhance their personal development in machine learning.
Ryan Ahmed