Mon, Aug 24, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
Machine Learning for Trading: From Research to Production
Stefan Jansen
Author, ML for Trading (3rd ed) · 9 case studies · Applied AI
Mon, Aug 24, 2026
4:00 PM UTC (30 minutes)
Virtual (Zoom)
Free to join
Go deeper with a course
Machine Learning for Trading: From Research to Production
Stefan Jansen
Author, ML for Trading (3rd ed) · 9 case studies · Applied AI
What you'll learn
Fix the research contract
Hold labels, features, folds, metrics, and strategy rules stable before comparing runs.
Diagnose the limiting uncertainty
Route the next test to signal, strategy translation, or economic viability.
Choose a controlled next test
Select the smallest comparison that could change a proceed, revise, monitor, or stop decision.
Why this topic matters
A model result does not determine the next experiment. This lesson shows how to hold the research contract fixed, diagnose the limiting uncertainty, and choose a controlled test across signal, strategy translation, and economic viability.
You'll learn from
Stefan Jansen
Author, ML for Trading · Founder, Applied AI · Investing since 2013
Stefan is the author of ML for Trading — the book and open-source companion code (19,000+ GitHub stars) that have become a practitioner reference for applying ML to financial markets. The 2026 third edition expands to nine cross-asset case studies, with a foreword by Antonio Gulli, Senior Director, Google.
He maintains the Zipline fork the quant community relies on, and built the six-library stack — data to live — behind the third edition's case studies. Investment partner since 2013, he has built trading platforms and live strategies across asset classes. In 2016 he founded Applied AI, which brings production ML to investment teams and other data-rich verticals. He has taught ML to 110,000+ professionals through DataCamp and General Assembly, incl. at Bloomberg and BlackRock.