Training

Python and R are the programming languages for machine learning. We’re hosting special training sessions for investors during our conferences world wide.

The training will show how to perform a dynamic portfolio back test using efficient machine learning tools. The training sessions will incorporate common ML libraries and emerging approaches. At the end of each training session, the instructors will introduce some packages for interpretability and some extensions will also be discussed.

Certification will be given after the sessions.

Training materials

Machine Learning in R

Access to materials Machine learning in R

RStudio has created special materials for our attendees. Create an account to access the materials.

Training video

Click on the video below to watch our training in ‘Machine Learning in R’.

 

Python for Machine Learning

Training video

Click on the video below to watch our training ‘Python for Machine Learning’.

 

Training Instructors

machineByte Europe instructors

Photo of Tony Guida

Tony Guida, executive director – senior quant research, RAM Active Investments

Tony Guida is executive director – senior quant research at RAM Active Investments. Before this, Tony was a senior investment manager in quantitative equity at the investment manager of a major UK pension fund in London, where he managed multifactor systematic equity portfolios.

During his career, he held such positions as senior consultant for smart beta and risk allocation at EDHEC RISK Scientific Beta and senior research and investment committee for Minimum Variance Strategies, where he led the factor investing research group for institutional clients, and a regular speaker at quant conferences.

Tony is Chair of machineByte ThinkTank EMEA and editor of the Journal of Machine Learning in Finance and also author of Big Data and Machine Learning in Investment.

Image Guillaume Coqueret

Guillaume Coqueret, associate professor of finance and data science, EMLYON

Guillaume Coqueret is an associate professor of finance and data science at EMLYON Business School. His research revolves around applications of numerical methods, especially in financial economics. His work has been published in journals such as Journal of Portfolio Management, Journal of Banking and Finance and Expert Systems with Applications.