Word2Vec is a popular technique in natural language processing that transforms words into numerical vectors. This allows computers to understand the meaning of words based on their context in large text datasets. By analyzing word co-occurrences, Word2Vec captures semantic relationships, enabling similar words to have similar vector representations. There are two main models in Word2Vec: Continuous Bag of Words (CBOW) and Skip-gram. The CBOW model predicts a target word based on its surrounding context, while the Skip-gram model does the opposite, predicting context words from a given target word. Both models help in creating meaningful word embeddings.