kavgan/nlp-in-practice resource

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1185 stars · 785 forks observed · 2026-08-28

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

A collection of Jupyter notebooks and Python scripts demonstrating practical NLP and text mining techniques, including Word2Vec, TF-IDF keyword extraction, text classification, and text preprocessing. It serves as starter code with accompanying tutorials for solving real-world text data problems.

Use cases

• learn how to train and use gensim word2vec embeddings
• extract keywords from text with tf-idf
• build a text classifier with logistic regression
• preprocess text with stemming, lemmatization, and stop word removal
• do word count on large files with pyspark
• load pre-trained GloVe and word2vec embeddings
• understand tfidftransformer vs tfidfvectorizer

When to choose

• you want hands-on starter code for common NLP tasks in Python
• you are learning text mining concepts through runnable notebooks
• you need simple examples of word embeddings, TF-IDF, or text classification

When to avoid

• you need a production-ready NLP library or maintained package
• you require a licensed dependency for commercial use (no license is provided)
• you need up-to-date code, as the repository has not been updated since 2020

Facets

learning-resource · maturity maintenance

nlp machine-learning data-science data-visualization machine-learning data-science tutorials python jupyter-notebooks word2vec tf-idf text-classification gensim pyspark text-preprocessing word-embeddings starter-code natural-language-processing

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