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ML Parameters Optimization: GridSearch, Bayesian, Random

Offered By: Coursera Project Network via Coursera

Tags

Machine Learning Courses Hyperparameter Optimization Courses Regression Models Courses Bayesian Optimization Courses

Course Description

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.

Syllabus

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

Taught by

Ryan Ahmed

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