hse-aml/natural-language-processing resource

Health v2 · maintenance only

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1189 stars · 1929 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook assignments for the Higher School of Economics Natural Language Processing course on Coursera. It covers classical and deep learning NLP approaches using Python, TensorFlow, NLTK, Scikit-learn, and Gensim, runnable locally, in Docker, or on Google Colab.

Use cases

• learn natural language processing with hands-on assignments
• practice NLP with TensorFlow and scikit-learn notebooks
• supplement a Coursera NLP course with code exercises
• study classical and deep learning text classification
• run NLP course assignments on Google Colab with free GPUs

When to choose

• you are enrolled in or following the HSE Coursera NLP course
• you want structured, graded-style NLP exercises in Python
• you prefer notebook-based learning with Colab GPU support

When to avoid

• you need a production NLP library or framework
• you want a maintained project with an active license and releases
• you are looking for up-to-date NLP techniques beyond the course scope

Facets

learning-resource · maturity maintenance

nlp machine-learning deep-learning machine-learning education tutorials python coursera jupyter-notebooks assignments hse tensorflow nltk gensim scikit-learn google-colab natural-language-processing web-server docker

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