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 straightforward, stakeholder-approved explanations that align with regulatory standards and business demands. This course equips you with practical tools to ensure transparency and compliance in AI-driven risk assessment.

4 to 360 hours flexible workload
valid certificate in your country
What will I learn?
Gain hands-on skills to develop, interpret, and oversee credit risk models confidently. Cover data analysis, feature preparation, and preprocessing, followed by training reliable, understandable models. Excel in overall and instance-specific explanations, fairness checks, bias identification, and correction. Produce straightforward reports, visual dashboards, and stories that meet stakeholder needs, aid choices, and comply with regulations.
Elevify advantages
Develop skills
- Develop transparent credit models: train, adjust, and record risk forecasts.
- Use SHAP and broad XAI methods: uncover vital factors and model patterns swiftly.
- Build ethical, regulation-compliant AI: spot bias, gauge effects, and devise remedies.
- Produce precise instance explanations: via SHAP, LIME, and hypothetical scenarios.
- Generate stakeholder-focused XAI outputs: including dashboards, APIs, and verifiable 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 workload.What our students say
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