Text Vectorization in NLP with Python | BoW, TF-IDF, Word2Vec, GloVe Explained

Unlock the power of text data! In this all-in-one tutorial, we’ll walk you through the most popular text vectorization techniques used in Natural Language Processing (NLP):
Colab:
🔹 Bag of Words (BoW)
🔹 TF-IDF (Term Frequency-Inverse Document Frequency)
🔹 Word2Vec
🔹 GloVe (Global Vectors)
🔹 Pre-trained Word Embeddings

You’ll learn: ✅ How each technique works
✅ Real-world Python implementation
✅ When to use which method
✅ Code demos using scikit-learn, Gensim, and pre-trained embeddings

Perfect for Data Science, NLP, and Machine Learning enthusiasts!

📌 Tools Used: Python, scikit-learn, Gensim, NumPy
📌 Level: Beginner to Intermediate