Credit Scoring with ML: A Structured Introduction
Machine learning methods applied to credit scoring have shifted from theoretical research to standard practice. The models in this program reflect that shift — built from data patterns, validated on real portfolios, and interpreted with tools that regulators and risk teams actually use.
about this program
Most introductory ML courses use image classification or text tasks. Credit scoring is different: the data is tabular, the class imbalance is severe, and the output must be defensible to non-technical stakeholders.
Starting from scratch
The first two weeks assume only spreadsheet-level familiarity with data. You will set up a working Python environment, load a loan dataset, and train a logistic regression model before the end of day one.
Explanations focus on intuition before math. Concepts like entropy, odds ratios, and ROC curves are introduced through examples drawn from lending, not abstract toy problems.
Building toward something real
By week four, you will have a complete scoring pipeline: data cleaning, feature selection, model training, and a simple scorecard that can be handed to a credit policy team.
A logistic regression scorecard built carefully often outperforms a complex model built carelessly. Simplicity is not a limitation here.
Support and feedback
Weekly live Q&A sessions with the instructor. Code submissions are reviewed with written feedback, not just automated test results. Progress is tracked through mini-projects, not multiple-choice quizzes.
program curriculum
structured by topic period and learning outcome
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Python for Credit Analysts
Week 1Environment setup, pandas basics, loading and inspecting a loan portfolio dataset.
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Understanding the Target Variable
Week 2Defining default, handling class imbalance with SMOTE and cost-sensitive learning.
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Logistic Regression Scorecards
Week 3Odds scaling, point allocation, interpreting coefficients in credit terms.
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Model Validation
Week 4Gini, KS statistic, lift curves, out-of-time validation strategy.
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Presenting to Stakeholders
Week 5Building a one-page model summary, explaining decisions without technical jargon, documentation templates.
ready to work with REAL scoring data?
The program runs on actual credit portfolio datasets under controlled conditions. You leave with code, documentation, and a validated model — not slides. Enrolment closes when seats fill.
22 seats remaining