Machine Learning University: Accelerated Natural Language Processing Class

Related tags

Machine Learning python machine-learning natural-language-processing deep-learning mxnet sklearn gluon
Overview

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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 from our YouTube channel.

Playlist

Course Overview

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

Lecture 1 Lecture 2 Lecture 3
Introduction to ML Tree-based Models Neural Networks
Intro to NLP and Text Processing Regression Models Word Embeddings
Bag of Words (BoW) Optimization-Regularization Recurrent Neural Networks (RNN)
K Nearest Neighbors (KNN) Hyperparameter Tuning Transformers
AWS AI/ML Services

Final Project: Practice working with a "real-world" NLP 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.

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.

Comments
  • Requirements file, updated sagemaker notebook and bug fixes

    Requirements file, updated sagemaker notebook and bug fixes

    • Requirements file added and used/tested with all notebooks
    • MLA-NLP-Lecture2-Sagemaker.ipynb: Sagemaker API calls are updated to the latest
    • The following bug fixes are done: 1: MLA-NLP-Lecture3-Neural-Networks-PyTorch.ipynb: Removed the unused mxnet imports 2: MLA-NLP-Lecture3-Recurrent-Neural-Networks-PyTorch.ipynb: Corrected the data path 3: NLP-Lecture3-Word-Vectors.ipynb: In section 4: words[labels].asmatrix() deprecated. Converted to words[labels].values

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by cemsaz 2
  • Update mlu-nlp.yml

    Update mlu-nlp.yml

    Issue #, if available: An old version of NumPy was used.

    Description of changes: Replaced with NumPy New Version(1.22.0) with an Old Version of NumPy(1.19.5).

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by pandyaved98 1
  • Added Studio Lab button at README.md

    Added Studio Lab button at README.md

    Issue #, if available:

    Description of changes:

    • Added Open SageMaker Studio Lab button at READMEmd.

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    opened by wildgeece96 1
  • Upgrade lib versions

    Upgrade lib versions

    Description of changes: 1-Library requirements are adjusted and an environment file: mlu-nlp.yml is added. 2- notebooks/PyTorch/MLA-NLP-Lecture3-Recurrent-Neural-Networks-PyTorch.ipynb notebook upgraded to the latest calls.

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by cemsaz 1
  • [PyTorch] Add MLA NLP

    [PyTorch] Add MLA NLP

    Issue #, if available:

    Description of changes: This PR adds the PyTorch version for Neural Networks and RNN notebook. All other notebooks do not involve use of gluon hence are same as before.

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.

    opened by AnirudhDagar 1
  • Fixed a typo in environment file

    Fixed a typo in environment file

    Description of changes: Fixed a typo in mlu-nlp.yml and added seaborn library to requirements and mlu-nlp.yml

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    opened by cemsaz 0
  • Kamba modern Canvas

    Kamba modern Canvas

    https://github.com/aws-samples/aws-machine-learning-university-accelerated-nlp Yousherou canvabest master, power school system Yusha'u. Garba Kamba ABCD Yousherou Gbest. Canva. Global Issue #, if available:

    • Kamba modern Canvas Dan Auta International University power then School system for this'll Scam Curriculum Fake Generation system Description of changes:

    By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. By yushau modern yousherou kamba modern Canvas Dan Auta international

    opened by Auta1994 0
  • Update README and logo

    Update README and logo

    Update README and logo

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    opened by cemsaz 0
  • Update README.md

    Update README.md

    Readme update

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    opened by cemsaz 0
  • files upload

    files upload

    Initial upload

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    opened by cemsaz 0
  • Bump numpy from 1.19.5 to 1.22.0

    Bump numpy from 1.19.5 to 1.22.0

    Bumps numpy from 1.19.5 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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