Explainable AI: Unlock the 'black box' of AI models
Local & Global Explainability | SHAP | LIME | Counterfactuals
Description
Imagine a scenario where a Machine Learning Engineer, armed with a sophisticated fraud detection model, is struggling to justify its outcomes to a non-technical team. Questions are fired from all corners:
"Why was this transaction flagged as fraudulent?"
"What factors led to this decision?"
"Can we trust these results?"
The ML engineer is at a loss - the model is a black box, and deciphering it seems like an enigma. If you've ever found yourself in a similar situation or have asked these kind of questions to your machine learning team, our course, "Explainable AI", is tailor-made for you.
We believe in teaching without detours, getting straight to the point and without beating around the bush. Our aim? To equip you with the skills to crack open the 'black box' of AI, making it transparent and trustworthy.
We illuminate the realm of explainability, dissecting why it's a cornerstone for any AI deployment. With a focus on both local and global explainability, we demonstrate how to dissect individual predictions and unravel the overall logic of models. The intriguing concept of counterfactuals is explored, painting a picture of alternative scenarios that could alter a model's decision.
We also dive deep into the world of SHAP (SHapley Additive exPlanations), an invaluable library that unearths the contributions of features in model predictions. By the end of this course, you'll be able to transform abstract AI outcomes into understandable, convincing explanations. So, let's together demystify AI and ensure it becomes an accountable and comprehensible tool in your arsenal!
What You Will Learn!
- What is explainer AI
- How ML models make decisions?
- What are local explainability and global explainability
- How to use SHAP and LIME in explaining model outcomes
- What is a counterfactual
Who Should Attend!
- Machine learning enthusiasts
- CxOs
- Users of ML models
- Anyone who has had trouble defending the outcome of a ML model
- Technologists
- Students