Predictive Models for Financial Risk is a short, practical course for financial analysts, interns, and early-career professionals who want to use supervised machine learning responsibly in finance. Many predictive models fail not because of poor algorithms, but because key workflow steps—data preparation, validation, or transparent communication—are skipped.
In this course, you’ll learn how to follow a complete supervised learning workflow, from defining a predictive question to evaluating results. You’ll build and test a decision tree classifier in Python, apply it to financial data, and report accuracy and insights in clear business language. Through short videos, guided readings, and hands-on labs, you’ll practice turning financial datasets into transparent, data-driven risk assessments. The course concludes with a project where you train and evaluate your own model, communicate performance results, and reflect on fairness and trust in financial predictions.
