Intro to Hyperparameter Tuning with Python
Offered By: Codecademy
Course Description
Overview
Improve machine learning models with hyperparameter tuning.
Hyperparameters are values that can be adjusted to improve a Machine Learning model. In this course, you will learn industry standard techniques for hyperparameter tuning, including Grid Search, Random Search, Bayesian Optimization, and Genetic Algorithms.
* Understand the role of hyperparameters
* Improve model performance with tuning
* Pick the best tuning method for a model
### Notes on Prerequisites
We recommend that you complete [Intro to Regularization with Python](https://codecademy.com/learn/intro-to-regularization-with-python) before completing this course.
Hyperparameters are values that can be adjusted to improve a Machine Learning model. In this course, you will learn industry standard techniques for hyperparameter tuning, including Grid Search, Random Search, Bayesian Optimization, and Genetic Algorithms.
* Understand the role of hyperparameters
* Improve model performance with tuning
* Pick the best tuning method for a model
### Notes on Prerequisites
We recommend that you complete [Intro to Regularization with Python](https://codecademy.com/learn/intro-to-regularization-with-python) before completing this course.
Syllabus
- Intro to Hyperparameter Tuning with Python: Learn about hyperparameter tuning methods in machine learning.
- Article: Hyperparameters in Machine Learning Models
- Lesson: Hyperparameter Tuning with `scikit-learn`
- Quiz: Hyperparameter Tuning
- Project: Classify Raisins with Hyperparameter Tuning!
- Informational: What's Next?
Taught by
Zoe Bachman
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