Data Science with Python: Enhancing Model Accuracy and Robustness
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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.
Author Name: Anand Saravanan
Author Description:
Anand is a software engineer and is enthusiastic about AI and its multidisciplinary applications. He has always had a passion for technology and how it can be used to help people and businesses. Excellence in various programming languages such as C++, Java, R, Python, SQL, Hadoop, etc., he is a man who has an itch to constantly learn and share his knowledge. He has worked with Fortune 500 companies in various sectors to help them effectively use the data at hand to help make better business dec… more
Table of Contents
- Course Overview
1min - Overfitting vs. Underfitting
8mins - Hyper-parameter Optimization and Cross Validation
13mins
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