Machine Learning for Credit Risk Python-Beginner to Advanced

About this course

This course contains the use of artificial intelligence.

Machine Learning is transforming the way banks and financial institutions assess credit risk, build predictive models, and make lending decisions. This course teaches you how to apply Machine Learning techniques to Probability of Default (PD), Loss Given Default (LGD), Exposure at Default (EAD), and Expected Credit Loss modelling using Python.

Designed for both beginners and experienced professionals, the course covers every step required to build production-ready credit risk solutions. You will learn not only how to code these models, but also how they fit into real-world risk management, IFRS 9 provisioning, Basel capital modelling, and stress-testing environments.

Throughout the program, you will build hands-on models using practical Python examples and clearly structured workflow lessons.

You will learn how to:

• Build supervised Machine Learning models for PD, LGD, and EAD predictions
• Perform data preparation, data cleaning, and feature engineering
• Apply model performance metrics such as ROC, KS, Gini, and Brier Score
• Evaluate and compare models using logistic regression, tree-based methods, and boosting algorithms
• Use model explainability techniques including SHAP-based interpretation
• Create ML pipelines to train, score, and monitor production models
• Set up basic deployment concepts such as APIs, dashboards, and data feeds
• Understand risk governance expectations for model validation and transparency
• Connect ML concepts to real-world credit risk decisioning and portfolio management

The course includes structured examples and practical case studies such as retail PD modelling, LGD estimation examples, EAD modelling challenges, and credit portfolio stress-testing demonstrations.

By the end of this course, you will be able to:

• Build real Machine Learning solutions in Python
• Understand end-to-end model development in a financial ris

Prices, discounts and availability are set by Udemy. We may earn a commission when you purchase through links on this site.