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the last tutorial we looked at what word embeddings are in this tutorial we are going to look at how word embeddings are calculated theyre calculated using two techniques one is supervised learning the other one is self supervised learning such as word to work or globe in this video we are going to look at supervised learning technique for word embeddings in the future video we will cover what to whack and glow and todays video will go over some some theory on how supervised learning works for word embedding and then well write some python code as well in last session we saw how you can represent words using the vectors like this where it can represent the meaning of word meaningfully for example here I have two cricket players Dhonian Cummins and one of the ways to represent the word vector is by using the features such as person healthy fit location and so on and when you do that what you find is the vectors for Dhoni and Cummins who are cricket player they look quite similar you