ML for Financial Data Structures

ML for Financial Data Structures

Course Description

This course aims to demonstrate how to approach financial data with machine learning algorithms. Due to the “unpredictable” nature of the data, straightforward fitting machine learning to “predict something in the future” will inevitably fail and lead to capital losses. The approach and toolkit shown in the course will help the economy and financial experts to build better and more reliable systems and will work as a shortcut for data science experts who never worked in the finance field before. In both cases, it will introduce to the students’ exceptional financial methods that are rarely discussed in classic machine learning materials which will broaden the mental models and applied toolkits.

Course tools

  • General Python scientific computing stack (NumPy, pandas, scipy, scikit-learn)


  • Intermediate applied mathematics skills
  • Intermediate scientific computing skills
  • A moderate understanding of financial markets is welcomed, but not necessary
Level of complexity of course Intermediate


Oleksandr Honchar Head of AI/ML @ Neurons Lab Mathematician by training and entrepreneur by trade. Alex has spent the first part of his career as an independent R&D specialist and lecturer. He helped to create AI-based products with different startups, has built profitable quantitative trading strategies, and regularly shared his learnings at his lectures and on his blog. Today Alex is Head of AI/ML and Partner at Neurons Lab, where he is boosting lab-to-market activities for deep tech startups and innovators Fields of interests: Artificial intelligence, applied mathematics, deep tech startups, quantitative finance, blockchain and web3 Contacts:  

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