- Course
- AI
Regression Model Explainability
Understand the “why” behind your regression models. This course will teach you how to explain linear regression models clearly and confidently using practical techniques like coefficient interpretation, residual analysis, and regularization.
What you'll learn
Explaining machine learning models can be just as challenging as building them.
In this course, Regression Model Explainability, you’ll learn to confidently interpret and communicate the behavior of linear regression models.
First, you’ll explore how to explain regression coefficients in context and understand what they reveal about relationships in your data.
Next, you’ll discover how to detect and address issues like multicolinearity and assumption violations and use regularization techniques like LASSO and ridge regression to improve model clarity.
Finally, you’ll learn how to explain regression models with non-linear transformations.
When you’re finished with this course, you’ll have the skills and knowledge of regression model analysis needed to build tools that are not only accurate, but also transparent, trustworthy, and aligned with real-word decision-making.
Table of contents
About the author
Daryle has developed products for Lockheed Martin, Tesla, and other companies across various industries. He also teaches data science and software engineering to young aspiring professionals.