XAI course
Master explainable AI for credit risk modelling: construct interpretable models, assess and reduce bias, set up monitoring and fairness reviews, and convert SHAP analysis into straightforward, stakeholder-approved explanations that fulfil regulatory standards and business demands. Discover practical techniques for data handling, model training, explainability tools like SHAP and LIME, bias mitigation strategies, and creating compliant reports that build trust in AI decisions.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain hands-on skills to develop, interpret, and oversee credit risk models reliably. Explore data, engineer features, preprocess effectively, and train trustworthy, understandable 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 overall XAI methods: uncover main factors and model patterns quickly.
- Develop fair, rule-following AI: spot bias, gauge effects, and apply fixes.
- Produce detailed local explanations: apply SHAP, LIME, and what-if scenarios.
- Prepare XAI reports for stakeholders: build dashboards, APIs, and reliable logs.
Suggested summary
Before starting, you can change the chapters and workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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