Machine Learning for Fraud Detection: From Rules to Models
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Technical Teams
Applied Machine Learning
Classification, regression, model evaluation and feature engineering on real-world datasets.
- 3–5 days
- Software, data and infrastructure teams
- On-site or online
- Certificate of attendance
What you will learn
- Choose the right machine learning approach for the problem
- Build classification and regression models
- Improve model performance through feature engineering
- Evaluate models with the right metrics and avoid overfitting
Course content
- The machine learning workflow and scikit-learn
- Classification and regression algorithms
- Tree-based models and gradient boosting
- Feature engineering and feature selection
- Model evaluation, cross-validation and imbalanced data
Who should attend
- Data scientists and analysts
- Developers working on AI projects
- Technical team leads
Prerequisites
Data analysis with Python (“Data Science with Python” or equivalent).
Course content is tailored to your organisation's needs. All programmes can be delivered on-site at your premises or online. Participants who complete a programme receive a certificate of attendance.
Frequently Asked Questions
Which algorithms are covered?
The methods most often used for business problems: classification and regression, tree-based models and gradient boosting.
Do you work with real datasets?
Yes, with real or realistic datasets; where appropriate, your anonymised data can be used.
Does it include finance and risk examples?
On request, yes. Industry examples such as credit risk, scoring or fraud detection can be added.
Does it cover deploying models?
The focus is building and evaluating models. For deployment and monitoring we recommend our MLOps programme.
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Tell us which programmes you are interested in, the number of participants and the preferred format, and we will prepare a tailored proposal.