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.
ML Parameters Optimization: GridSearch, Bayesian, Random
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Beginner level
No prior experience required
2 hours
Learn at your own pace
Hands-on learning
What you'll learn
Understand the difference between hyperparameters optimization techniques such as GridSearch, Bayesian & Random Search Optimization Techniques.
Optimize ML model hyperparameters in Scikit-Learn using GridSearch, Bayesian & Random Search Optimization Techniques.
Evaluate several trained regression models performance using various Key Performance Indicators (KPIs).
Skills you'll practice
Details to know
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Taught in English
No downloads or installation required
Only available on desktop
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About this Guided Project
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Understand the Problem Statement
Import Libraries and Datasets
Practice Opportunity #1 [Optional]:
Practice Opportunity #2 [Optional]
Train an XG-Boost Algorithm Without Optimization
Practice Opportunity #3 [Optional]
Strategy #1: Optimization Using GridSearch
Strategy #2: Optimization Using Random Search
Strategy #3: Optimization Using Bayesian Optimizer
Final Capstone Project
Final Capstone Project Solution
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How you'll learn
Skill-based, hands-on learning
Practice new skills by completing job-related tasks.
Expert guidance
Follow along with pre-recorded videos from experts using a unique side-by-side interface.
No downloads or installation required
Access the tools and resources you need in a pre-configured cloud workspace.
Available only on desktop
This Guided Project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
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