Specialist track, course code ML
Machine Learning for Finance
Machine learning in finance, applied with the controls that markets demand: purged validation, leakage checks and honest costs.
For machine learning in finance, the data is the hard part. Signals are weak, samples are small relative to the noise, observations overlap in time, and the process changes as other traders learn the same patterns. Methods that work on images and text fail here unless validation is designed for these conditions.
What does the machine learning track cover?
A free course reviews what machine learning has and has not achieved in trading. Machine Learning for Trading covers feature construction, tree ensembles and walk-forward validation on market data. Financial Machine Learning, at Researcher level, covers purged and embargoed cross-validation, meta-labeling, sample weighting and backtest overfitting, then neural networks, sequence models and transformers judged against simple baselines, and reinforcement learning for execution and allocation, with why reward design matters. Alternative Data and NLP covers language models on news, filings and earnings calls, and the due diligence a dataset needs.
Where does this track lead?
To the Certificate in Machine Learning for Trading, which ends in a capstone model validated out of sample with costs charged.
Open courses in this track
Free primers. Read, watch and work the exercises before you spend anything.
Practitioner courses in this track
Lab-based courses. You build, test and cost real strategies and systems on market data.
Researcher courses in this track
Proof-level courses. Stochastic models, estimation theory and the literature behind each method.
- Alternative Data and NLP for Finance Alternative data and natural language processing as funds use them: turning news, filings and earnings calls into signals with language models, judging satellite, card and web data, and testing whether any of it adds return once costs and timing are honest.
- Financial Machine Learning: Deep and Reinforcement Learning The research methods developed for machine learning on financial data: sampling with information-driven bars, triple-barrier labels and meta-labeling, sample weights for overlapping outcomes, purged cross-validation, feature importance, and statistics of backtest overfitting. It also teaches deep learning for finance and reinforcement learning for trading.
Credentials that use this track
- Certificate in Algorithmic TradingSeven courses and a capstone that take a strategy from idea to a costed, sized and validated system, traded on paper and reported the way a trading desk would read it.
- Certificate in Statistical ArbitrageSeven courses and a capstone on market-neutral trading: time series foundations, pairs and baskets, cointegration, regimes, costs and the validation that keeps spread strategies honest.
- Certificate in Machine Learning for TradingFive courses and a capstone on applying machine learning to markets without fooling yourself: labeling, features, purged validation, deep and language models, regimes and reinforcement learning.
- Professional Diploma in Algorithmic TradingThe top credential on the practitioner path. Twenty-one courses and an 80-hour capstone covering everything between the mathematics and a book you could defend to a risk committee.
- Fellowship in Computational FinanceThe top credential on the researcher path. Twenty-one courses and a dissertation supervised in writing and examined by two faculty members.
Questions applicants ask
Does AI trading work?
Some machine learning methods add value in specific tasks such as execution, risk forecasting and combining many weak signals. Most published results on price prediction do not survive costs and proper validation. The free course AI Trading: What Works and What Does Not covers the evidence.
What background do I need?
Python and statistics. The Certificate in Machine Learning for Trading includes Time Series Analysis and Financial Econometrics for that reason, and assumes Python and SQL for Finance or the same skill gained elsewhere.