Udemy - Accelerate Hyperparameter Tuning with Multifidelity Model...

Accelerate Hyperparameter Tuning with Multifidelity Models

https://WebToolTip.com

Published 9/2026
Created by Soledad Galli, Train in Data Team
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 9 Lectures ( 1h 5m ) | Size: 434.2 MB

Use successive halving in scikit-learn to allocate resources progressively and find strong configurations more efficient

What you'll learn
⚡ Explain how multi-fidelity optimization and successive halving work
⚡ Combine successive halving with Grid Search and Random Search
⚡ Configure resource budgets, reduction factors, and candidate allocation
⚡ Analyze successive-halving results and select strong configurations
⚡ - Build resource-efficient hyperparameter tuning workflows for tabular models

Requirements
❗ Basic knowledge of Python and common machine learning workflows
❗ Familiarity with hyperparameters, cross-validation, Grid Search, and Random Search
❗ Some experience with scikit-learn and tabular machine learning models