# hse-aml/natural-language-processing

Resources for "Natural Language Processing" Coursera course.

Repository: https://github.com/hse-aml/natural-language-processing
Canonical: https://ross.abutalabs.com/products/natural-language-processing
Homepage: https://www.coursera.org/learn/language-processing
Language: Jupyter Notebook
License Family: other
Topics: natural-language-processing
Last push: 2022-12-21T03:03:01+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3202, "days_push": 1351, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1189, forks 1929 (observed 2026-08-28T04:03:55.764009+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: maintenance
- function: nlp, machine-learning, deep-learning
- domain: machine-learning, education, tutorials
- platform: python
- tags: coursera, jupyter-notebooks, assignments, hse, tensorflow, nltk, gensim, scikit-learn, google-colab, natural-language-processing, web-server, docker

## Member repositories
- hse-aml/natural-language-processing (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:55.764009+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:23:11.640274+00:00, confidence not recorded.
  - readme: https://github.com/hse-aml/natural-language-processing (fetched 2026-08-28T04:03:55.764009+00:00, sha 389344a01e77)
- Data as of 2026-08-30T08:39:29.467469+00:00.