Share this link via
Or copy link
Overview
“Intro to Testing Machine Learning Models” is a practical course designed to introduce learners to essential techniques for evaluating and validating machine learning models. Testing ML models is a critical step to ensure their accuracy, robustness, and generalizability before deployment. Participants explore different types of testing, including train-test splits, cross-validation, and A/B testing. The course covers key evaluation metrics for various problem types—such as accuracy, precision, recall, F1 score for classification, and mean squared error for regression. Learners also study...
Syllabus
- Intro to Testing Machine Learning Models
- Chapter 1 - What is Machine Learning?
- Chapter 2.1 - Build a Machine Learning Model (Workflow Steps)
- Chapter 2.2 - Build a Machine Learning Model (Colab)
- Chapter 3 - Where do Testers Fit in Machine Learning?
- Chapter 4 - Adversarial Attacks
- Chapter 5.1 - Behavioral Testing (Concepts and Train NLP Model)
- Chapter 5.2 - Behavioral Testing (Test the Model)
- Chapter 6 - Fair and Responsible AI
- Chapter 7 - Machine Learning Models in Production
Instructor
Carlos Kidman
Reviews
No reviews yet. Be the first to review!
Alternative Courses
Introduction to AI Programming Welcome to the AI programming with...
Free
You must be logged in to submit a review.