June Online Session: Explainable AI & Model Interpretability (SHAP & LIME)

### June Online Session: Explainable AI & Model Interpretability (SHAP & LIME)

💡 Can you trust your machine learning models? Learn how to make AI explainable!
As machine learning models become more complex, understanding how they make decisions is critical—especially in high-stakes industries like finance, healthcare, and law. In this session, we’ll dive into Explainable AI (XAI) and explore tools like SHAP (SHapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) to interpret ML models.

### What You’ll Learn:

✅ Why model interpretability is crucial in machine learning
✅ Understanding black-box models vs. interpretable models
✅ How SHAP values help explain individual predictions
✅ Using LIME for model-agnostic explanations
✅ Best practices for making AI more transparent and ethical

### Who Should Attend?

📊 Data Scientists & ML Engineers who want to improve model transparency
🔍 Business & Domain Experts who need to understand ML decisions
🚀 Anyone working with AI models in real-world applications
📌 Prerequisites: Basic knowledge of machine learning

📅 Date: [Insert Date]
🕕 Time: [Insert Time]
🌍 Where: Online (Link provided upon registration)

🔗 Register Now & Learn How to Make AI Explainable!
#ExplainableAI #XAI #MachineLearning #DataScience #SHAP #LIME #MLClub