In this video, we demystify Explainable AI (XAI)-the key to making AI systems more transparent, trustworthy, and understandable. Alongside in-depth explanations, we’ve included practical coding exercises for SHAP, LIME, and Permutation Feature Importance to help you implement these powerful techniques in your own projects.
✨ What You’ll Learn:
* What is XAI?
* Why traditional AI models lack interpretability?
* Top methods for XAI: SHAP, LIME, and Permutation Feature Importance.
* Hands-on coding examples to implement these techniques step-by-step.
* The future of XAI and its role in building trust and compliance.
🖥️ Coding Included: This video features practical exercises where we:
* Implement SHAP to visualize model explanations.
* Use LIME to interpret individual AI predictions.
* Apply PFI to rank feature importance globally.
👉 Watch till the end for actionable insights and coding demos that make XAI easier to understand and apply. Don’t forget to like, share, and subscribe to support our channel and help us create more valuable content for you!
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