Building Financial Intelligence Through Machine Learning
Where did this project start
Domain began in 2025 when a group of quantitative analysts and educators noticed a gap. Universities taught theory, bootcamps taught coding, but few programs connected machine learning with real financial decision-making. Students could build models but struggled to apply them to portfolio management, risk assessment, or market behavior.
We created lectures that walk through actual financial datasets, explain why certain algorithms work better for time series prediction, and show how practitioners validate models before deployment. The focus is on clarity, not buzzwords. Each module builds on the previous one, so learners develop a structured understanding rather than memorizing disconnected techniques.
The lectures are designed for professionals transitioning into quantitative roles, finance students exploring data science, and developers entering fintech. Content is delivered in English to reach learners across regions, with examples and formatting that reflect Canadian financial contexts.
The people behind the curriculum
Domain is led by professionals who have worked in quantitative finance, data engineering, and educational design. They build content that reflects industry practices and teach concepts they use in their own work. Each instructor brings a different perspective, which keeps the material grounded and relevant.
Félix Bérubé
Félix spent eight years building predictive models for asset management firms before shifting to education. He designs the core curriculum and teaches modules on supervised learning, feature engineering, and model validation. His lectures include real datasets from equities, commodities, and credit markets.
Siobhan Flett
Siobhan builds the hands-on exercises and coding assignments. She has a background in software engineering and quantitative research, and focuses on making complex algorithms accessible. Her work ensures that learners can implement what they learn without getting stuck on syntax or environment setup.