Ensemble Techniques for High Performance Machine Learning - Gilberto Titericz (Ople)

In this PAPIs.io LATAM talk, Gilberto Titericz presented some ensembling techniques to boost the performance of machine learning algorithms and improve the quality of the predictions. He also remarked the importance of diversity between models that are trained in same data to avoid overfitting.

Gilberto was born in Brazil and got his Masters in Electrical Engineering in 2008. Worked for more than 10 years developing electronics hardware and changed his area to data science after won some important Machine Learning competitions in sites like Kaggle (#2 Kaggler in the world🤩) and now is focused in improving his ML skills. Today Gilberto works for Airbnb using ML to help build the best platform for hosts and guests all around the world.