courseraАнализ данных

Optimize ML Models: Hyperparameter Tuning

Курс от Coursera

Средний≈ 3.5 чАнглийский

О курсе

Optimize ML Models: Hyperparameter Tuning gives you the practical skills to move from “good enough” models to models that perform reliably at scale. You’ll learn how default hyperparameters shape model behavior, how computational complexity affects training cost, and why structured tuning methods outperform guesswork. Through short videos, hands-on practice, and a guided GridSearchCV project, you’ll build a complete workflow for selecting, evaluating, and explaining tuned model configurations. By the end of the course, you’ll know how to design effective search spaces, run systematic tuning experiments, interpret cross-validated results, and save tuned parameters for real ML pipelines—all essential skills for modern machine learning and AI roles.

Навыки, которые вы освоите

Model Optimization Performance Tuning Machine Learning Methods Model Training Applied Machine Learning Scikit Learn (Machine Learning Library)MLOps (Machine Learning Operations)Machine Learning Algorithms Model Evaluation

Программа курса

1 модулей · 17 учебных материалов

01Optimize ML Models: Hyperparameter Tuning 17 материалов

Understand Defaults: Hyperparameters and Algorithm Complexity

Why Defaults Matter More Than You Think?DIALOGUE Welcome and Course IntroductionВидео What Are Hyperparameters? Understanding Defaults Across AlgorithmsВидео Three Essential Hyperparameter Tuning Techniques for Better Machine Learning ModelsЧтение

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