Data Science with Python: Enhancing Model Accuracy and Robustness
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.
Table of contents
About the author
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