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Machine Learning University: Decision Trees and Ensemble Methods Class

This repository contains slides, notebooks, and datasets for the Machine Learning University (MLU) Decision Trees and Ensemble Methods class. Our mission is to make Machine Learning accessible to everyone. We have courses available across many topics of machine learning and believe knowledge of ML can be a key enabler for success. This class is designed to help you get started with tree based models, learn about widely used Machine Learning techniques and apply them to real-world problems.

YouTube

Watch all class video recordings in this YouTube playlist from our YouTube channel.

Course Overview

There are five lectures, one final project and five assignments for this class.

Lecture 1

title	studio lab
Decision Trees	-
Impurity Functions	-
CART Algorithm	-
Regularization

Lecture 2

title	studio lab
Bias-variance trade-off	-
Error Decomposition	-
Extra Trees Algorithm
Bias-variance and Randomized Ensembles	-

Lecture 3

title	studio lab
Boostrapping
Bagging	Bagging tree correlation
Random Forests

Lecture 4

title	studio lab
Random Forest Proximities	-
Some use cases for Proximities	-
Feature Importance in Trees
Feature Importance in Random Forests

Lecture 5

title	studio lab
Boosting
Gradient Boosting	-
XGBoost, LightGBM and CatBoost	CatBoost LightGBM

Final Project

title	studio lab
Final Project

Final Project: Practice working with a "real-world" computer vision dataset for the final project. Final project dataset is in the data/final_project folder. For more details on the final project, check out this notebook.

Interactives/Visuals

Interested in visual, interactive explanations of core machine learning concepts? Check out our MLU-Explain articles to learn at your own pace!

Including relevant articles for this course: Decision Trees, Random Forest, and the Bias Variance Tradeoff.

Contribute

If you would like to contribute to the project, see CONTRIBUTING for more information.

License

The license for this repository depends on the section. Data set for the course is being provided to you by permission of Amazon and is subject to the terms of the Amazon License and Access. You are expressly prohibited from copying, modifying, selling, exporting or using this data set in any way other than for the purpose of completing this course. The lecture slides are released under the CC-BY-SA-4.0 License. The code examples are released under the MIT-0 License. See each section's LICENSE file for details.