XAI course
Master explainable AI for credit risk modelling: construct interpretable models, assess and address bias, set up monitoring and fairness checks, and transform SHAP analysis into straightforward explanations that align with business goals and regulatory standards. Build confidence in your AI deployments with practical tools for transparency and accountability.

from 4 to 360h flexible workload
valid certificate in your country
What will I learn?
Gain hands-on skills to develop, interpret, and track credit risk models reliably. Cover data analysis, feature preparation, and model training for clear, accurate predictions. Dive into explainability techniques, fairness checks, bias identification, and fixes. Produce stakeholder-friendly reports, dashboards, and stories that drive decisions and comply with regulations.
Elevify advantages
Develop skills
- Build transparent credit risk models: train, calibrate, and record predictions.
- Use SHAP and global XAI methods: uncover vital factors and model patterns swiftly.
- Develop fair, regulation-compliant AI: spot bias, gauge effects, and implement remedies.
- Craft precise local explanations: apply SHAP, LIME, and counterfactual scenarios.
- Produce stakeholder-approved XAI outputs: dashboards, APIs, and verifiable records.
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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