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.