FeaturedUdemy
Hyperparameter Tuning in Python
4.9(827)25k studentsINTERMEDIATEEnglish
Korshub's take
Overview
This intermediate Python course teaches practical methodologies for automated hyperparameter optimization using Scikit-Learn. You explore the difference between parameters and hyperparameters, practice exhaustive Grid Search on credit card default data, compare it with Random Search, and advance to informed search techniques like Bayesian and Genetic algorithms.
Who It Is For
Intermediate Python programmers and data scientists who want to systematically optimize machine learning model performance and parameters.
Key Takeaways
- Understand the distinction between parameters and hyperparameters in Scikit-Learn.
- Perform Grid Search and Random Search to discover optimal hyperparameter combinations.
- Implement informed optimization techniques including Bayesian and Genetic algorithms.
About this course
As a data or machine learning scientist, building powerful machine learning models depends heavily on the set of hyperparameters used. But with increasingly complex models with lots of options, how do you efficiently find the best settings for your particular problem? The answer is hyperparameter tuning!
Learn the difference between hyperparameters and parameters and best practices for setting and analyzing hyperparameter values. This foundation will prepare you to understand the significance of hyperparameters in machine learning models.
You will be introduced to Random Search, and learn about its advantages over Grid Search, such as efficiency in large parameter spaces.
These informed search techniques are demonstrated through practical examples, allowing you to compare and contrast them with uninformed search methods. By the end, you will have a comprehensive understanding of how to optimize hyperparameters effectively to improve model performance.
Hyperparameters vs. parameters
Gain practical experience using various methodologies for automated hyperparameter tuning in Python with Scikit-Learn.Learn the difference between hyperparameters and parameters and best practices for setting and analyzing hyperparameter values. This foundation will prepare you to understand the significance of hyperparameters in machine learning models.
Grid search
Master several hyperparameter tuning techniques, starting with Grid Search. Using credit card default data, you will practice conducting Grid Search to exhaustively search for the best hyperparameter combinations and interpret the results.You will be introduced to Random Search, and learn about its advantages over Grid Search, such as efficiency in large parameter spaces.
Informed search
In the final part of the course, you will explore advanced optimization methods, such as Bayesian and Genetic algorithms.These informed search techniques are demonstrated through practical examples, allowing you to compare and contrast them with uninformed search methods. By the end, you will have a comprehensive understanding of how to optimize hyperparameters effectively to improve model performance.
Advertisement