Course Description
Ensembles are everywhere in Machine Learning. The RandomForest algorithm is arguably one of the most popular and successful Machine Learning approaches of all time. Dropout – famous regularisation strategy can be thought of as a bagging ensemble of many Deep Neural Networks. XGBoost acquired a reputation of a silver bullet in Kaggle competitions. Despite their immense popularity, ensemble methods remain a mystery for many. Their seeming incomprehensive complexity intimidates ML practitioners, preventing them from utilizing ensembles at their full capacity. In this lecture we will break down and carefully examine all the most important ensemble learning concepts, such as bagging, boosting, stacking, and blending. We will use small examples to demonstrate the inner workings of each ensemble type. Participants will be able to put their ensemble learning understanding into practice in a Colab notebook.Course tools
- Python via Collaboratory
Prerequisites
- Knowledge of Python (numpy, pandas, scikit-learn)
- Understanding of the basic machine learning (around supervised learning and overfitting)