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Get Competitive with Driverless AI

Recent world #1 Kaggle Grandmaster and Research Data Scientist at, Marios Michailidis, will delve into the competitive edge that Driverless AI brings out of the box.

Driverless AI can easily score in the top 5% in popular data science challenges against thousands of participants in a matter of minutes with limited processing power.

Apart from the actual predictions, one can use Driverless AI data munging and derived knowledge of the data to build even more powerful models.

This webinar discusses how Driverless AI can get competitive scores in popular Kaggle challenges. Also, Marios will explain the concepts of hyper-parameter tuning and stacking and how they help to make stronger predictions.


Former world no.1 Kaggle Grandmaster, Marios Michailidis, is now a Research Data Scientist at He is finishing his PhD in machine learning at the University College London (UCL) with a focus on ensemble modeling and his previous education entails a B.Sc in Accounting Finance from the University of Macedonia in Greece and an M.Sc. in Risk Management from the University of Southampton. He has gained exposure in marketing and credit sectors in the UK market and has successfully led multiple analytics’ projects based on a wide array of themes.

Before, Marios held the position of Senior Personalization Data Scientist at dunnhumby where his main role was to improve existing algorithms, research benefits of advanced machine learning methods, and provide data insights. He created a matrix factorization library in Java along with a demo version of personalized search capability. Prior to dunnhumby, Marios has held positions of importance at iQor, Capita, British Pearl, and Ey-Zein.

At a personal level, he is the creator and administrator of KazAnova, a freeware GUI for quick credit scoring and data mining which is made absolutely in Java. In addition, he is also the creator of StackNet Meta-Modelling Framework.

Please view the slides here:

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