Data Science with Python: Enhancing Model Accuracy and Robustness





Published 5/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 48 KHz
Language: English | Size: 54.07 MB | Duration: 22m 29s

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.


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.

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