Hyperparameter Tuning in Python

Starting at USD 39.00
4 hours duration

Learn to tune hyperparameters in Python.

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.

About Provider

DataCamp
DataCamp
Online Education Provider ยท 410 courses
Description
DataCamp is an online learning platform that offers interactive courses and tutorials for data science and analytics. It provides a wide range of courses covering topics such as Python, R, SQL, machine learning, data visualization, and more. The platform offers a hands-on learning experience through coding exercises and projects, allowing users to practice and apply their skills in real-world scenarios. DataCamp also offers a personalized learning experience with adaptive learning technology that adjusts the course content based on the user's skill level and progress. It is widely used by individuals, professionals, and organizations to enhance their data science skills and stay up-to-date with the latest trends and technologies in the field.
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