Data Science with Python: Enhancing Model Accuracy and Robustness
Data Science with Python: Enhancing Model Accuracy and Robustness
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 22m | 53.7 MB
Instructor: Anand Saravanan
.MP4, AVC, 1920x1080, 30 fps | English, AAC, 2 Ch | 22m | 53.7 MB
Instructor: Anand Saravanan
Simply creating a machine learning model is not enough to gain the best insights on the data. This course will teach you how to enhance a model through hyper-parameter tuning and other methods.
What you'll learn
How do you take a generic machine learning model and make it more accurate on your specific data? In this course, Data Science with Python: Enhancing Model Accuracy and Robustness, you’ll gain the ability to take an existing machine-learning model and learn how to tune the hyper-parameters to make it more accurate.
First, you’ll explore overfitting and underfitting with a linear regression model. Next, you’ll discover the various hyper-parameters of decision trees and how to optimize them to a specific dataset. You'll also see how to validate the dataset. Finally, you’ll learn how to save the model so we can use it again in the future.
When you’re finished with this course, you’ll have the skills and knowledge of hyper-parameter tuning needed to enhance machine learning models.
Data Science with Python: Enhancing Model Accuracy and Robustness
