Ensemble Machine Learning With Python: 7-Day Mini-Course

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Ensemble learning refers to machine learning models that combine the predictions from two or more models. Ensembles are an advanced approach to machine learning that are often used when the capability and skill of the predictions are more important than using a simple and understandable model. As such, they are often used by top and winning participants in machine learning competitions like the One Million Dollar Netflix Prize and Kaggle Competitions. Modern machine learning libraries like scikit-learn Python provide a suite of advanced ensemble learning methods that are easy to configure and use correctly without data leakage, a common concern when using ensemble algorithms. In this crash course, you will discover how you can get started and confidently bring ensemble learning algorithms to your predictive modeling project with Python in seven days.

Author(s): Jason Brownlee
Series: Machine Learning Mastery
Edition: 1.1
Publisher: Independently Published
Year: 2021

Language: English
Pages: 17

Before We Get Started...
Lesson 01: What Is Ensemble Learning?
Lesson 02: Bagging Ensembles
Lesson 03: Random Forest Ensemble
Lesson 04: AdaBoost Ensemble
Lesson 05: Gradient Boosting Ensemble
Lesson 06: Voting Ensemble
Lesson 07: Stacking Ensemble
Final Word Before You Go...