Word vectors are useful building blocks in many applications. They capture and encode the semantic relationships between words. As a consequence, they lead to the transformation of words into a sequence of numbers, forming a dense vector that is well-suited for training deep learning models. In this chapter, we will take a detailed look at the various approaches to building such semantically useful word embeddings.