Hyperparameter Tuning in Python
Engineering and Technology
Short Description
Learn to tune hyperparameters in Python.
Long Description
The effectiveness of machine learning models greatly relies on the selection of appropriate hyperparameters. However, as models become more complex and offer numerous options, it becomes challenging to efficiently determine the optimal settings for a specific problem. This course aims to provide practical training in employing various automated hyperparameter tuning methodologies in Python using Scikit Learn. These methodologies include Grid Search, Random Search, as well as advanced optimization techniques such as Bayesian and Genetic algorithms. Throughout the course, you will work with a dataset focused on predicting credit card defaults, enabling you to enhance the efficiency and effectiveness of your machine learning model development skills significantly.
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Course Details
- Duration
- 4 hours
- Format
- Short Course
- Price
- USD39.00
- Course Link More Information