What you’ll learn

  • How to actually use Deep Learning on Energy Datasets, step-by-step, from scratch
  • Clarifying key concepts about Deep Learning models application to Energy
  • Specialized analysis on all stages – starting with preprocessing until forecasts till year 2050
  • Theoretical foundations along with practical explanations
  • Part of the giannelos dot com official certificate for high-tech projects.


What is the course about:

The course shows – step by step and in great detail – how to apply Deep Learning, on an energy dataset. Using this algorithm, we generate forecasts all the way to 2050.  This requires fine tuning of all hyperparameters, including the selection of the degree of the polynomial. In depth sensitivity analyses are performed and demonstrate the importance of the forecasting error, which we evaluate using proxies and statistical measures.


I am a research fellow at Imperial College London, and I have been part of high-tech projects at the intersection of Academia & Industry for over 10 years, prior to, during & after my Ph.D. I am also the founder of the giannelos dot com program in data science.

  • Doctor of Philosophy (Ph.D.) in Analytics & Mathematical Optimization applied to Energy Investments, from Imperial College London, and Master of Engineering (M. Eng.) in Power Systems and Economics.


  • Prerequisites: The course Data Science Code that appears all the time at Workplace.
  • Every detail is explained, so that you won’t have to search online, or guess. In the end, you will feel confident in your knowledge and skills.
  • We start from scratch so that you do not need to have done any preparatory work in advance at all.  Just follow what is shown on screen, because we go slowly and explain everything in detail.

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