Learn Credit Scoring, Machine Learning, and Python
This course is designed to give you practical, hands-on skills and a clear, structured path to understanding credit scoring with machine learning – a vital topic in today’s data-driven finance and fintech sectors.
What you’ll learn
- Develop a solid understanding of credit scoring and risk-based pricing, and how these concepts are used in real-world lending decisions.
- Build, train, and evaluate machine learning models using Scikit-learn and Python.
- Explore and prepare credit data using pandas and Jupyter Notebook.
- Interpret model outputs and performance metrics, including confusion matrices, ROC curves, AUC, and cost-based evaluation.
- Understand the impact of false positives and false negatives, and how to balance them in credit scoring use cases.
- Apply cross-validation techniques, divergence analysis, and risk-based grouping.
- Use Scikit-learn Pipelines to streamline preprocessing and ensure reproducible, production-ready workflows.
- Translate technical results into business insights, empowering data-driven decision-making in credit risk and beyond.
Course Content
- Welcome to the Course –> 1 lecture • 3min.
- Credit Scoring and Risk-Based Pricing –> 5 lectures • 8min.
- Introduction to Data Exploration and Analysis –> 5 lectures • 24min.
- Machine Learning in Credit Scoring –> 25 lectures • 2hr 52min.
Requirements
This course is designed to give you practical, hands-on skills and a clear, structured path to understanding credit scoring with machine learning – a vital topic in today’s data-driven finance and fintech sectors.
Led by a data scientist with over 12 years of experience in analytics, machine learning, and developing AI-powered applications, this course focuses on real-world implementation – not just theory.
This course will give you the tools and mindset you need to build, evaluate, and understand credit scoring models using Python and Scikit-learn.
Tools and Technologies:
- Python
- Jupyter Notebook
- Pandas
- Matplotlib & Seaborn
- Scikit-learn
This course is project-driven, beginner-friendly, and highly practical. Each topic includes step-by-step demonstrations and visual explanations to help you confidently apply what you learn.
By the end of this course, you’ll not only be able to build a credit scoring model, but also understand the business implications of your predictions – a skill that’s essential in regulated industries like lending and finance.
At the same time, credit scoring serves as an excellent real-world case study for learning machine learning. So even if your goal is to break into machine learning more broadly – beyond finance – you’ll gain valuable experience working with data, applying algorithms to solve classification problems, and interpreting model performance in a practical context.