Name: Machine Learning with Tree-Based Models in Python
Rating: 4.863070539419087 (241 reviews)

Machine Learning with Tree-Based Models in Python

IntermediateSkill Level

4.8+

Updated 08/2024

In this course, you'll learn how to use tree-based models and ensembles for regression and classification using scikit-learn.

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Course Description

Decision trees are supervised learning models used for problems involving classification and regression. Tree models present a high flexibility that comes at a price: on one hand, trees are able to capture complex non-linear relationships; on the other hand, they are prone to memorizing the noise present in a dataset. By aggregating the predictions of trees that are trained differently, ensemble methods take advantage of the flexibility of trees while reducing their tendency to memorize noise. Ensemble methods are used across a variety of fields and have a proven track record of winning many machine learning competitions. In this course, you'll learn how to use Python to train decision trees and tree-based models with the user-friendly scikit-learn machine learning library. You'll understand the advantages and shortcomings of trees and demonstrate how ensembling can alleviate these shortcomings, all while practicing on real-world datasets. Finally, you'll also understand how to tune the most influential hyperparameters in order to get the most out of your models.

Prerequisites

Supervised Learning with scikit-learn

Classification and Regression Trees

Course Description

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FAQs

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Join over .css-nklxlk{color:var(--wf-brand--main, #03EF62);}17 million learners and start Machine Learning with Tree-Based Models in Python today!

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Join over 17 million learners and start Machine Learning with Tree-Based Models in Python today!