Financial modelling with AI/machine learning

About this course

Participants work through the AI-driven financial modelling process, from financial data analysis, time-series preparation and asset-price modelling to portfolio optimisation, risk modelling and forecasting. Topics include H2O AutoML, XGBoost, LightGBM, TensorFlow, PyTorch, backtrader, NLP sentiment analysis and reinforcement learning for trading. Teaching combines concise theory, live coding demonstrations and hands-on projects using industry datasets and tools. Learners develop automated trading and forecasting pipelines, interactive Streamlit dashboards and cloud-based deployment models, with short assignments and strategy refinement exercises outside class.

What you'll learn

• Design, test and deploy machine-learning-driven trading systems.
• Apply technical analysis alongside automated machine-learning methods.
• Develop forecasting models using deep-learning techniques.
• Automate portfolio optimisation and risk-management workflows.
• Use NLP tools and alternative financial data to analyse markets.
• Evaluate ethical, regulatory and interpretability issues in financial AI.

Is this course right for you?

The course is intended for Intermediate learners with familiarity with Python and foundational knowledge of financial markets or machine learning. It may suit professionals in finance, fintech or data science. Participation includes live coding, project work and short exercises to complete outside class.

• Python familiarity and foundational finance or machine-learning knowledge are expected.
• Experience with pandas, NumPy and scikit-learn is beneficial but not mandatory.
• The course includes additional assignments and strategy-refinement work outside class.