Started in 2014, we've focused on one thing: helping students actually understand ensemble methods in finance. No fluff, just practical workshops that work.
Most machine learning courses dump theory on you without showing how it connects to actual financial problems. We got tired of that disconnect.
Our workshops focus on the specific challenges you'll face: handling noisy financial data, combining multiple models without overfitting, and validating results that actually matter for trading decisions. Everything is hands-on, step-by-step, and based on what works in practice.
We're not trying to turn you into a data scientist overnight. We're just showing you techniques that solve real problems in portfolio optimization, risk assessment, and prediction—areas where ensemble methods actually shine.
We cut out the academic formality and focus on what you need to actually apply these methods.
Work with actual financial time series, not cleaned-up examples. Learn to handle the messiness that real data brings.
Each workshop has you constructing models from scratch. You'll understand why boosting works differently than bagging because you'll implement both.
We cap enrollment so you can get direct feedback on your code and approach. No getting lost in a crowd of hundreds.
Access materials whenever works for you. Move faster through concepts you grasp quickly, spend more time on tricky parts.
Every example relates to financial modeling. We don't waste time on generic classification problems that don't translate.
By the end, you'll have working code and a clear understanding of when to use random forests versus gradient boosting in your own projects.
Spent 8 years building quantitative models for hedge funds before teaching. Specializes in making complex ensemble techniques understandable and applicable to real trading scenarios.
Check out our current workshops or get in touch if you have questions about which program fits your background and goals.