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up to this point weamp;#39;ve talked about vectorizing documents using bag of words approaches whether itamp;#39;s binary frequency or TF IDF in this module weamp;#39;ll go down one level and talk about how to vectorize words themselves and why thatamp;#39;s useful specifically weamp;#39;ll cover something called Static embeddings and how they can help us capture some aspects of a wordamp;#39;s meaning I underline the word static because the NLP world has moved on to more powerful contextualized embeddings which we will cover later in this course and so weamp;#39;re not going to spend too much time on static embeddings but itamp;#39;s important to understand a few Core Concepts here because they form the foundation for further material theyamp;#39;re also still very cool and can be useful for building base models letamp;#39;s start by talking about the simplest way to vectorize Words which is one hot encoding one hot encoding is a concept weamp;#39;ve already encountered whe
