Mastering Hyperparameter Optimization for Machine Learning

How to select the optimal hyperparameters and enhance the performance of machine learning models using numerous techniques.

Intermediate

37 Lessons

Certificate of Completion

How to select the optimal hyperparameters and enhance the performance of machine learning models using numerous techniques.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

1 Project

1 Assessment

10 Playgrounds

5 Quizzes

This course includes

1 Project

1 Assessment

10 Playgrounds

5 Quizzes

Course Overview

Machine learning models excel in classification, regression, anomaly detection, language translation, and more. Optimizing hyperparameters can enhance the performance of most machine learning models. This course will equip you with the skills to optimize hyperparameters for various machine learning models. You’ll begin with the introduction of hyperparameters and understand the need for optimizing them. Using a loan approval dataset for binary classification, you’ll explore both random and grid search met...Show More

TAKEAWAY SKILLS

Python

Data Science

Machine Learning

What You'll Learn

Familiarity with hyperparameter optimization methods, including random search, grid search, and sequential model-based optimization

Hands-on experience configuring, implementing, and evaluating hyperparameter optimization techniques using Python

Understanding the advantages and disadvantages of the various hyperparameter optimization methods

Working knowledge of Python libraries such as scikit-learn, TPOT, scikit-optimize, and Optuna for hyperparameter optimization

What You'll Learn

Familiarity with hyperparameter optimization methods, including random search, grid search, and sequential model-based optimization

Hands-on experience configuring, implementing, and evaluating hyperparameter optimization techniques using Python

Understanding the advantages and disadvantages of the various hyperparameter optimization methods

Working knowledge of Python libraries such as scikit-learn, TPOT, scikit-optimize, and Optuna for hyperparameter optimization

Course Content

Introduction

Course Overview Introduction to Hyperparameters Introduction to Hyperparameter Optimization Introduction to the Dataset for the Course

Random Search Method

Introduction to the Random Search Method How Does Random Search Work Data Preparation Random Search Using Logistic Regression Random Search Using the Random Forest Algorithm Advantages and Disadvantages of Random Search Method Quiz on Random Search Method

Grid Search Method

Introduction to Grid Search Method How Does Grid Search Work Grid Search Using Logistic Regression Grid Search Using Random Forest Algorithm Advantages and Disadvantages of the Grid Search Method Quiz on Grid Search Method

Sequential Model-Based Optimization Method

Introduction to the Sequential Model-Based Optimization Method How Does Sequential Model-Based Optimisation Work Sequential Model-Based Optimization using K-Nearest Neighbors SMBO Using Histogram-Based Gradient Boosting Advantages and Disadvantages of the SMBO Method Quiz on the Sequential Model-Based Optimization Method

Tree-Structured Parzen Estimators Method

Introduction to the Tree-Structured Parzen Estimator Method How Does the Tree-Structured Parzen Estimator Method Work Tree-Structured Parzen Estimator Method Using KNN TSPE Method Using Histogram-Based Gradient Boosting Advantages and Disadvantages of the TSPE Method Quiz on the Tree-Structured Parzen Estimator (TPE) Method

Genetic Algorithm

6 Lessons

Evaluate Hyperparameter Optimization Concepts

Assessment

Optimizing ML Model for Promotion Selection

Project

Conclusion

1 Lesson

Appendix

1 Lesson

COURSE AUTHOR

How You'll Learn

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