Congratulations

1. Congratulations

Congratulations on completing our journey through Explainable AI! From foundational techniques to advanced concepts, you've gained a toolkit to make AI transparent and trustworthy.

2. Chapter 1

In Chapter 1, you learned the basics of explainable AI, starting by exploring key principles and terminologies. Moreover, you learned about model-specific explainability approaches for linear and tree-based models.

3. Chapter 2

Chapter 2 expanded your toolkit with model-agnostic techniques like permutation importance and SHAP, broadening your skills across various AI models.

4. Chapter 3

In Chapter 3, you learned about local explainability, where you applied SHAP and LIME to individual data points, enhancing your ability to provide detailed explanations for individual samples on different kinds of datasets.

5. Chapter 4

We wrapped up with advanced topics in Chapter 4, exploring metrics that assess the quality of explanations and delving into the explainability of unsupervised models and generative AI.

6. What's next?

With these skills, now you can apply explainability techniques to diverse AI models,

7. What's next?

enhance AI transparency and trust,

8. What's next?

explore AI fairness and bias detection,

9. What's next?

and stay updated on emerging tools and methods in explainable AI.

10. Congratulations!

Congratulations once again, and thank you for embarking on this journey with me! I encourage you to keep exploring, questioning, and applying these concepts in your life to create more transparent and trustworthy AI systems. Best of luck!

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