Thu, Jul 30, 2026
3:00 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
Thu, Jul 30, 2026
3:00 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
See the book as one research workflow
Connect market structure, data, prediction, strategy design, backtesting, and deployment across 27 chapters.
Choose a reading path for your goals
Find the chapters and resources that matter most whether you are learning, researching, or building production systems.
Connect case studies to working code
See how nine cross-asset case studies, companion notebooks, and six libraries turn concepts into practice.
Why this topic matters
The third edition is more than a model catalog. It connects market structure, data, prediction, strategy design, backtesting, and deployment through nine cross-asset case studies. This guide will help you see that architecture, choose the right path through the material, and connect the book to its open-source code.
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.