verified
Verified Information • Last Updated Mar 2026
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.
Duration
3 Months
Institution
Coursera
Format
Online
Eligibility Criteria
school
Academic Foundation
A recognized Bachelor’s degree or high school equivalent required for admission into Coursera.
language
Language Proficiency
English proficiency required. IELTS, TOEFL, or standard medium-of-instruction certificates accepted.
Detailed Fees Breakdown
Base Tuition Fee
$347
Total Est. Investment
$347
Scholarships and early-bird waivers may apply. Contact admissions for exact institutional fees.
Academic Trajectory
Program Outcome
Graduates of the ML Parameters Optimization: GridSearch, Bayesian, Random program at Coursera are equipped with global perspectives, ready to excel in international markets and top-tier career opportunities.