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

Machine Learning University: Accelerated Tabular Data Class
https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab

deep-learning gluon machine-learning mxnet python sklearn tabular-data

Last synced: 22 days ago
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Machine Learning University: Accelerated Tabular Data Class

Host: GitHub
URL: https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab
Owner: aws-samples
License: other
Created: 2020-07-30T20:19:50.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2024-09-16T16:23:05.000Z (about 2 months ago)
Last Synced: 2024-09-29T16:20:57.205Z (about 1 month ago)
Topics: deep-learning, gluon, machine-learning, mxnet, python, sklearn, tabular-data
Language: Jupyter Notebook
Homepage:
Size: 38.5 MB
Stars: 1,009
Watchers: 65
Forks: 314
Open Issues: 4
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

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awesome-studio-lab-jp - テーブルデータ

README

        ![logo](data/MLU_Logo.png)

## Machine Learning University: Accelerated Tabular Data Class

This repository contains __slides__, __notebooks__, and __datasets__ for the __Machine Learning University (MLU) Accelerated Tabular Data__ 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 tabular data (spreadsheet-like tables), learn about widely used Machine Learning techniques for tabular data, and apply them to real-world problems.

## YouTube

Watch all Tabular Data class video recordings in this [YouTube playlist](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuVQZCYf_ZnMoIWLLKGx9Mi2) from our [YouTube channel](https://www.youtube.com/channel/UC12LqyqTQYbXatYS9AA7Nuw/playlists).

[![Playlist](https://img.youtube.com/vi/kj-sPC6pai4/0.jpg)](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuVQZCYf_ZnMoIWLLKGx9Mi2)

## Course Overview

There are three lectures and one final project for this class.

Lecture 1

| title | studio lab |

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

| Introduction to ML | - |

| Sample ML Model | - |

| Model Evaluation | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY1-MODEL.ipynb) |

| Exploratory Data Analysis | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY1-EDA.ipynb) |

| K Nearest Neighbors (KNN) | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY1-KNN.ipynb) |

| Final Project | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY1-FINAL.ipynb) |

Lecture 2

| title | studio lab |

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

|Feature Engineering | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY2-TEXT-PROCESS.ipynb) |

| Tree-based Models | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY2-TREE.ipynb) |

| Bagging | - |

| Hyperparameter Tuning | - |

| AWS AI/ML Services |[![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY2-SAGEMAKER.ipynb)  |

Lecture 3 

| title | studio lab |

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

| Optimization | - |

| Regression Models | - |

| Boosting | - |

| Neural Networks |NN [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY3-NN.ipynb) 
 MXNet [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY3-MXNET.ipynb)|

| AutoML |[![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY3-AUTOML.ipynb) |

**Final Project:** Practice working with a "real-world" tabular dataset for the final project. Final project dataset is in the [data/final_project folder](https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab/tree/master/data/final_project). For more details on the final project, check out [this notebook](https://github.com/aws-samples/aws-machine-learning-university-accelerated-tab/blob/master/notebooks/MLA-TAB-DAY1-FINAL.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! 

## 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.