XAI course
Master explainable AI for credit risk modelling: construct interpretable models, assess and address bias, set up monitoring and fairness reviews, and convert SHAP analysis into simple, stakeholder-friendly explanations that align with business goals and regulatory demands. Build confidence in your AI decisions with practical tools for transparency and compliance.

flexible workload of 4 to 360h
valid certificate in your country
What will I learn?
Gain hands-on skills to develop, interpret, and track credit risk models reliably. Explore data, engineer features, preprocess effectively, and build trustworthy, explainable models. Excel in global and local explanations, fairness checks, bias spotting, and fixes. Produce straightforward reports, dashboards, and stories that meet stakeholder needs, aid choices, and comply with rules.
Elevify advantages
Develop skills
- Build transparent credit risk models: train, adjust, and record predictions clearly.
- Use SHAP and global XAI methods: uncover main factors and model patterns quickly.
- Develop fair, rule-compliant AI: spot bias, gauge effects, and apply fixes.
- Craft precise local explanations: apply SHAP, LIME, and what-if scenarios.
- Produce stakeholder-approved XAI outputs: dashboards, APIs, and solid audit records.
Suggested summary
Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workloadWhat our students say
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