courseraАнализ данных

ML Parameters Optimization: GridSearch, Bayesian, Random

Курс от Coursera

Начальный≈ 1.9 чАнглийский

О курсе

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.

Навыки, которые вы освоите

Model Optimization Model Evaluation Fine-tuning Machine Learning Regression Analysis Applied Machine Learning Scikit Learn (Machine Learning Library)Data Analysis

Программа курса

1 модулей · 4 учебных материалов

01Project Overview4 материалов

Your Learning Journey

Project OverviewЧтение ML Parameters Optimization: GridSearch, Bayesian, RandomЛабораторная Assess Your KnowledgeЗадание Course End Survey - We appreciate your feedback!

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Dr. Ryan Ahmed

Professor & AI Expert (1M+ Students)