Word embeddings are numerical representations of words in a vector space, generated by machine learning algorithms. They capture the semantic meaning and relationships between words. Popular word embedding models include Word2Vec, GloVe, and FastText. Word embeddings are often used as inputs to NLP models to improve their performance.