
Sign up to save your podcasts
Or
Stefan Jansen is the author of the widely read ‘Machine Learning For Algorithmic Trading’. He is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy and developing machine learning solutions. Before his current venture, he was a partner at Infusive, an international investment firm, where he built the predictive analytics and investment research practice. He also was a senior executive at Rev Worldwide, a global fintech company focused on payments. Earlier, he advised Central Banks in emerging markets, and consulted for the World Bank. In this podcast we discuss what benefits does machine learning bring that other techniques don’t have, t he challenge of using machine learning in finance, u ses for ChatGPT and LLMs, and much more.
Follow us here for more amazing insights:
https://macrohive.com/home-prime/
https://twitter.com/Macro_Hive
https://www.linkedin.com/company/macro-hive
4.5
9090 ratings
Stefan Jansen is the author of the widely read ‘Machine Learning For Algorithmic Trading’. He is the founder and Lead Data Scientist at Applied AI. He advises Fortune 500 companies, investment firms and startups across industries on data & AI strategy and developing machine learning solutions. Before his current venture, he was a partner at Infusive, an international investment firm, where he built the predictive analytics and investment research practice. He also was a senior executive at Rev Worldwide, a global fintech company focused on payments. Earlier, he advised Central Banks in emerging markets, and consulted for the World Bank. In this podcast we discuss what benefits does machine learning bring that other techniques don’t have, t he challenge of using machine learning in finance, u ses for ChatGPT and LLMs, and much more.
Follow us here for more amazing insights:
https://macrohive.com/home-prime/
https://twitter.com/Macro_Hive
https://www.linkedin.com/company/macro-hive
586 Listeners
1,768 Listeners
3,072 Listeners
376 Listeners
931 Listeners
1,445 Listeners
665 Listeners
913 Listeners
80 Listeners
362 Listeners
112 Listeners
273 Listeners
211 Listeners
28 Listeners
90 Listeners