Ensemble models in machine learning with Python

In this



practical



course, we are going to focus on



ensemble models



in



supervised machine learning



using Python programming language.

Ensemble models are a particular kind of machine learning model that mixes



several models



. The general idea is that a team of models can increase the performance of a single one, both in terms of stability (i.e.



variance



) and in terms of accuracy (i.e.



bias



). The most common ensemble models are Random Forests and Gradient Boosting Decision Trees, which are explained extensively in the lessons of this course. Other types of ensemble models are voting and stacking, which are more complex procedures that can increase the performance of a model.

With this course, you are going to learn:

1. What bias-variance tradeoff is and how to deal with it
2. Bagging and some bagging models (like
   
   
   
   Random Forest
   
   
   
   )
3. Boosting and some boosting models (Like
   
   
   
   XGBoost
   
   
   
   or
   
   
   
   AdaBoost
   
   
   
   )
4. Voting
5. Stacking

All the lessons of this course start with a brief introduction and end with a



practical example



of



Python programming language



and its powerful



sci-kit-learn



library. The environment that will be used is



Jupyter



, which is a standard in the data science industry. All the Jupyter notebooks are



downloadable



.

This course is part of my



Supervised Machine Learning in Python



online course, so you’ll find some lessons that are already included in the larger course.