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

Machine Learning University: Accelerated Natural Language Processing Class
https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp

deep-learning gluon machine-learning mxnet natural-language-processing python sklearn

Last synced: about 23 hours ago
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Machine Learning University: Accelerated Natural Language Processing Class

Host: GitHub
URL: https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp
Owner: aws-samples
License: other
Created: 2020-08-07T00:40:22.000Z (over 4 years ago)
Default Branch: main
Last Pushed: 2024-10-13T00:49:31.000Z (2 months ago)
Last Synced: 2024-12-04T16:05:24.722Z (8 days ago)
Topics: deep-learning, gluon, machine-learning, mxnet, natural-language-processing, python, sklearn
Language: Jupyter Notebook
Homepage:
Size: 77.4 MB
Stars: 2,354
Watchers: 178
Forks: 611
Open Issues: 0
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 - 自然言語処理

README

        ![logo](data/MLU_Logo.png)

## Machine Learning University: Accelerated Natural Language Processing Class

This repository contains __slides__, __notebooks__ and __datasets__ for the __Machine Learning University (MLU) Accelerated Natural Language Processing__ 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 Natural Language Processing (NLP), learn widely used techniques and apply them on real-world problems.

## YouTube

Watch all NLP class video recordings in this [YouTube playlist](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuWfAq8Pt6PBYlck4OprHXsw) from our [YouTube channel](https://www.youtube.com/channel/UC12LqyqTQYbXatYS9AA7Nuw/playlists).

[![Playlist](https://img.youtube.com/vi/0FXKbEgz-uU/0.jpg)](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuWfAq8Pt6PBYlck4OprHXsw)

## Course Overview

There are three lectures and one final project in this class. Course overview is below.

Lecture 1

| title | studio lab |

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

| Introduction to ML | - |

| Intro to NLP and Text Processing | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-nlp/blob/main/notebooks/MLA-NLP-Lecture1-Text-Process.ipynb)|

| Bag of Words (BoW) | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-nlp/blob/main/notebooks/MLA-NLP-Lecture1-BOW.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-nlp/blob/main/notebooks/MLA-NLP-Lecture1-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-nlp/blob/main/notebooks/MLA-NLP-Lecture1-Final-Project.ipynb)

Lecture 2

| title | studio lab |

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

| 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-nlp/blob/main/notebooks/MLA-NLP-Lecture2-Tree-Models.ipynb)|

| Regression Models |Linear Regression [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-nlp/blob/main/notebooks/MLA-NLP-Lecture2-Linear-Regression.ipynb) 
 Logistic Regression [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-nlp/blob/main/notebooks/MLA-NLP-Lecture2-Logistic-Regression.ipynb) |

| Optimization-Regularization | - |

| Hyperparameter Tuning | - |

| 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-nlp/blob/main/notebooks/MLA-NLP-Lecture2-Final-Project.ipynb)|

Lecture 3

| title | studio lab |

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

| Neural Networks | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-nlp/blob/main/notebooks/MLA-NLP-Lecture3-Neural-Networks-PyTorch.ipynb) |

| Word Embeddings | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github/aws-samples/aws-machine-learning-university-accelerated-nlp/blob/main/notebooks/MLA-NLP-Lecture3-Word-Vectors.ipynb)|

| Recurrent Neural Networks (RNN) | [![Open In Studio Lab](https://studiolab.sagemaker.aws/studiolab.svg)](https://studiolab.sagemaker.aws/import/github//aws-samples/aws-machine-learning-university-accelerated-nlp/blob/main/notebooks/MLA-NLP-Lecture3-Recurrent-Neural-Networks-PyTorch.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-nlp/blob/main/notebooks/MLA-NLP-Lecture3-Final-Project.ipynb)|

__Final Project:__ Practice working with a "real-world" NLP 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-nlp/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-accelerated-nlp/blob/main/notebooks/MLA-NLP-Lecture1-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! 

## 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.