Natural Language Processing (NLP) involves enabling computers to understand human language by combining computational linguistics with Machine Learning and Deep Learning models. A crucial step in NLP is vectorization, which converts text data into numerical vectors that machine learning models can interpret. Key vectorization techniques include the Bag of Words, which creates vectors based on word frequency without considering context; TF-IDF, which adjusts word importance by considering their frequency across documents; Word2Vec, which uses neural networks to produce contextually aware word embeddings; GloVe, which captures both local and global statistics through co-occurrence matrices; and FastText, which improves on word embeddings by utilizing character-level information, allowing for generalization to unknown words. These techniques play a vital role in building robust models for tasks such as information retrieval, word similarity, and text classification, with each method offering unique advantages based on the specific NLP challenge at hand.