What you would learn in Ensemble models in machine learning with Python course?
For this practical course, we will concentrate on models grouped in machine learning supervised using Python programming language.
Ensemble models comprise a specific machine learning model that blends different models. The basic idea is that a group of models can enhance the performance of just one model both in terms of stability (i.e., variation) and also in the sense of precision (i.e., bias). The most popular models for ensembles include Random Forests and Gradient Boosting Decision Trees, which are detailed in the course's lessons. Course. Other models that are part of an ensemble are stacking and voting, which are more complicated methods that can enhance the effectiveness of a model.
Through this course, you're going to be taught:
What is the bias-variance tradeoff, and how can you deal with it
Bagging models, including bagging bags (like Random Forest)
The Boosting feature and some models for boosting (Like XGBoostor AdaBoost)
Voting
Stacking
The lessons in this course begin with a brief introduction. They finish with the real-world demonstration of the Python programming language and its impressive scikit-learn library. The platform that will be used for this course is Jupyter which is an industry-standard in the field of data science. The entire Jupyter notebooks are available for download. Available for download.
It's part of my supervised machine Learning using Python Online Course, and you'll be able to find several lessons that are part of the course.
Course Content:
- Bias variance tradeoff
- What models of the ensemble are
- Bagging and random forests
- Boosting and XGBoost
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