https://github.com/runtime-error786/text-vectorization

This repository demonstrates various text vectorization techniques including Bag of Words (BoW), TF-IDF, N-grams, and Word2Vec (CBOW,SKIPGRAM) using nltk,Gensim and Scikit-Learn. The steps outlined here show how to convert textual data into numerical vectors, which are essential for machine learning models.
https://github.com/runtime-error786/text-vectorization

Last synced: 4 months ago
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This repository demonstrates various text vectorization techniques including Bag of Words (BoW), TF-IDF, N-grams, and Word2Vec (CBOW,SKIPGRAM) using nltk,Gensim and Scikit-Learn. The steps outlined here show how to convert textual data into numerical vectors, which are essential for machine learning models.

• Host: GitHub
• URL: https://github.com/runtime-error786/text-vectorization
• Owner: runtime-error786
• Created: 2025-01-01T10:52:06.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-02T21:47:46.000Z (about 1 year ago)
• Last Synced: 2025-03-23T19:17:26.126Z (10 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 56.1 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Text Vectorization Techniques using nltk,Gensim and Scikit-Learn

This repository demonstrates various text vectorization techniques including Bag of Words (BoW), TF-IDF, N-grams, and Word2Vec (CBOW) using nltk,Gensim and Scikit-Learn. The steps outlined here show how to convert textual data into numerical vectors, which are essential for machine learning models.Word2Vec is a popular word embedding technique that uses either Continuous Bag of Words (CBOW) or Skip-gram model to learn vector representations of words based on their context.The CBOW model predicts the target word based on context words, while the Skip-gram model does the reverse by using a word to predict its surrounding context.