learning program
MACHINE Learning for Scoring Models
A structured set of courses covering the methods, tools, and real-world applications behind credit scoring and risk classification using machine learning. Each course targets a specific stage of that workflow.
all courses
AVAILABLE tracks
Machine Learning IntermediateML Scoring Models: From Raw Data to Credit Decisions
A technical course covering the full pipeline for building machine learning scoring models — from feature engineering to model validation and regulatory compliance.
Credit Risk BeginnerCredit Scoring with ML: A Structured Introduction
For analysts stepping into data science — learn to build interpretable scoring models without prior ML experience, using Python and real lending datasets.
MLOps & Risk ProfessionalAdvanced Risk Scoring: Gradient Boosting, Fairness, and Production
A practitioner-level program on deploying gradient boosting scoring models in regulated financial environments — covering fairness auditing, drift detection, and MLOps basics.
Behavioral Analytics IntermediateBehavioral Scoring Models: Predicting Risk Over Time
Learn to build behavioral scoring models using account transaction history, payment patterns, and time-series features — distinct from application scoring in both method and data.
Alternative Data Intermediate / AdvancedAlternative Data in ML Scoring: Methods and Limitations
A focused course on integrating non-traditional data sources — telco, open banking, rental history — into credit scoring models, with honest treatment of data quality and regulatory risk.
how courses are built
what the PROGRAM covers
Each course in the program targets a specific phase of scoring model development — from raw data preparation through to model validation and deployment. The curriculum draws on real lending datasets and risk classification scenarios rather than synthetic examples.
Core curriculum checklist
Technical prerequisites covered
about the program
scoring MODELS built from actual data
The program started in 2020 with the observation that most ML courses teach classification in abstract terms — accuracy on toy datasets, not Gini coefficients on real loan portfolios. Courses here use credit bureau files, loan origination records, and repayment histories as the working material throughout. Participants work in Python environments that mirror production risk team setups, not cleaned academic benchmarks.