What you would learn in Machine Learning and AI Foundations: Producing Explainable AI (XAI) and Interpretable Machine Learning Solutions course?
Data scientists and machine-learning professionals need to keep up with the industry's most recent techniques and methods. The instructor Keith McCormick shows you how to create explicable AI (XAI) and interpretable machine learning (IML) methods in the course.
Learn about the reasons why they need to use XAI has been growing in recent years. Discover the various methods and techniques used for XAI and IML, and IML, and the best time and method to utilize both. Keith guides you through the issues and possibilities of black-box models, demonstrating how to add an element of transparency into your model by using real-world examples to illustrate how to use the simple-to-learn and freely available KNIME Analytics Platform. When you finish this course, you'll better understand the concepts behind XAI and IML methods for local and global explanations.
Course Content:
- Introduction
- 1. What Are XAI and IML?
- 2. Why Isolating a Variable’s Contribution Is Difficult
- 3. Black Box Model 101
- 4. Introduction to KNIME for XAI and IML
- 5. XAI Techniques: Global Explanations
- 6. Techniques for Local Explanations
- 7. IML Techniques
- Conclusion
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