An Introduction to Derivatives

Machine learning is increasingly being employed in financial services to spot underlying patterns in data that are difficult to discern using conventional analysis techniques.  This course covers Machine Learning Fundamentals, provides an overview of using Python in Excel, and leaves delegates with a basic functional toolset for building Machine Learning models in Excel and/or Python.

Who should attend? 

This is perhaps best answered by a few examples of what machine learning is used for in the financial services arena.  Please note that there’s no deep dive into any one topic and that not all of these will be covered on the course. 

 

Common Applications of Machine Learning in Financial Services: 

  • Credit risk  
  • Credit scoring  
  • Debt collection  
  • Fraud detection  
  • Insurance underwriting 
  • Investments  
  • Portfolio management  
  • Trading (equities, forex etc 

 

Learning Modules: 

  • Machine learning introduction 
  • Build a machine learning model directly in Excel 
  • Machine learning with Python 
  • Combining Python and Excel 
  • General pitfalls 

Prerequisites: 

  • Excel proficiency: Intermediate or above 
  • Excel version: 2013 or later (desktop versions only)  
  • Windows 10 
  • Sufficient rights to install software on the course* 
  • Software required* 
  • Python 
  • Python modules 
  • PyXLL – Excel AddIn FREE trial version (NB. Only install 2-3 days prior to the workshop start) 

 

*The software installation forms part of the course; however a manual will be provided to delegates who need their IT department to pre-install software. 

 

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