What you'll learn:
- Rapidly evaluate machine learning models for performance
- Identify and address model drift
- Debug production ML models
- Identify and address possible ML bias issues
Please note - this course is being reworked and is now lecture only. The free application, hands-on exercises, and quizzes have been removed while the course is being revamped. If you would prefer to take the full version of the course after it is revamped, please check back in Fall/Winter 2024.
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Want to skill up your ability to test and debug machine learning models? Ready to be a powerful contributor to the AIera, the next great wave in software and technology?
Get taught by leading instructors who have previously taught at Carnegie Mellon University and Stanford University, and who have provided training to thousands of students from around the globe, including hot startups and major global corporations:
You will learn the analytics that you need to drive model performance
You will understand how to create an automated test harness for easier, more effective MLtesting
You will learn why AIexplainability is the key to understanding the key mechanics of your model and to rapid debugging
Understand what Shapley Values are, why they are so important, and how to make the most of them
You will be able to identify the types of drift that can derail model performance
You will learn how to debug model performance challenges
You will be able to understand how to evaluate model fairness and identify when bias is occurring - and then address it
Testimonials from the live, virtual version of the course:
"This is what you would pay thousand of dollars for at a university." - Mike
"Excellent course!!! Super thanks to Professor Datta, Josh, Arri, and Rick!! :D" - Trevia
"Thank you so very much. I learned a ton. Great job!" - K. M.
"Fantastic series. Great explanations and great product. Thank you." - Santosh
"Thank you everyone to make this course available... wonderful sessions!" - Chris
