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https://github.com/aws-samples/aws-machine-learning-university-dte

Machine Learning University: Decision Trees and Ensemble Methods
https://github.com/aws-samples/aws-machine-learning-university-dte

boosting catboost decision-trees lightgbm machine-learning random-forest tabular-data xgboost

Last synced: 26 days ago
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Machine Learning University: Decision Trees and Ensemble Methods

Host: GitHub
URL: https://github.com/aws-samples/aws-machine-learning-university-dte
Owner: aws-samples
License: other
Created: 2020-12-18T04:18:10.000Z (almost 4 years ago)
Default Branch: main
Last Pushed: 2023-11-02T21:31:22.000Z (about 1 year ago)
Last Synced: 2024-08-03T10:01:32.988Z (4 months ago)
Topics: boosting, catboost, decision-trees, lightgbm, machine-learning, random-forest, tabular-data, xgboost
Language: Jupyter Notebook
Homepage:
Size: 26.4 MB
Stars: 231
Watchers: 16
Forks: 91
Open Issues: 1
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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README

        ![logo](data/MLU_Logo.png)

## 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](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuXrj9crYtU_XaYh3Jac4x0p) from our [YouTube channel](https://www.youtube.com/channel/UC12LqyqTQYbXatYS9AA7Nuw/playlists).

[![Playlist](https://img.youtube.com/vi/DtX1hN0FRfk/0.jpg)](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuXrj9crYtU_XaYh3Jac4x0p)

## 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 | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_1/DTE-LECTURE-1-PRUNE.ipynb)|

Lecture 2

| title | studio lab |

| :---: | ---: |

| Bias-variance trade-off | - |

| Error Decomposition | - |

| Extra Trees Algorithm | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_2/DTE-LECTURE-2-TREE-VARIANCE.ipynb)|

| Bias-variance and Randomized Ensembles | - |

Lecture 3

| title | studio lab |

| :---: | ---: |

| Boostrapping | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_3/DTE-LECTURE-3-BOOTSTRAP.ipynb)|

| Bagging |Bagging [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_3/DTE-LECTURE-3-BAGGING-OVERFIT.ipynb) 
 tree correlation [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_3/DTE-LECTURE-3-TREE-CORRELATION.ipynb)|

| Random Forests | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_3/DTE-LECTURE-3-RANDOM-FOREST.ipynb)|

Lecture 4

| title | studio lab |

| :---: | ---: |

| Random Forest Proximities | - |

| Some use cases for Proximities | - |

| Feature Importance in Trees | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_4/DTE-LECTURE-4-PERMUTATION-FEATURE-IMP.ipynb)|

| Feature Importance in Random Forests |[![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_4/DTE-LECTURE-4-FEATURE-IMPORTANCE.ipynb) |

Lecture 5

| title | studio lab |

| :---: | ---: |

| Boosting | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_5/DTE-LECTURE-5-BOOSTING.ipynb)|

| Gradient Boosting | - |

| XGBoost, LightGBM and CatBoost | CatBoost [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_5/DTE-LECTURE-5-CATBOOST.ipynb) 
 LightGBM [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/lecture_5/DTE-LECTURE-5-LIGHTGBM.ipynb)|

Final Project

| title | studio lab |

| :---: | ---: |

| Final Project | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/final_project/DTE-FINAL-PROJECT.ipynb)|

__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](https://github.com/aws-samples/aws-machine-learning-university-dte/tree/main/data/final_project). For more details on the final project, check out [this notebook](https://github.com/aws-samples/aws-machine-learning-university-dte/blob/main/notebooks/final_project/DTE-FINAL-PROJECT.ipynb).

## Interactives/Visuals

Interested in visual, interactive explanations of core machine learning concepts? Check out our [MLU-Explain articles](https://mlu-explain.github.io/) to learn at your own pace! 

Including relevant articles for this course: [Decision Trees](https://mlu-explain.github.io/decision-tree/), [Random Forest](https://mlu-explain.github.io/random-forest/), and the [Bias Variance Tradeoff](https://mlu-explain.github.io/bias-variance/).

## Contribute

If you would like to contribute to the project, see [CONTRIBUTING](CONTRIBUTING.md) 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](https://www.amazon.com/gp/help/customer/display.html?nodeId=201909000). 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.