This week you'll learn about the Introduction to the Bag-of-Words (BoW) Model.

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As we move into the second tutorial of this series, I want to preface by saying that we have indeed developed many algorithms without outperforming Bag-of-Words by a country mile. But that doesn't discredit the fact that Bag-of-Words will always remain a big milestone as one ventures into understanding the idea of representational space in Natural Language Processing (NLP). 

When Bag-of-Words was conceived, it was a much-needed win for NLP's progress, which repeatedly stalled due to the behemoth constraints faced in this domain. The idea also opened new approaches to conceiving proper embedding space.

The big picture: Today, we will learn about Bag-of-Words, an algorithm that helps us model text data by treating sentences as vectors and ignoring paradigms like grammar. 

How it works: The meaning of a sentence is determined by the occurrence and non-occurrence of the present vocabulary. 

Our thoughts: In the long run, to move into the more advanced topics, Bag-of-Words might provide you with another outlook on embedding spaces in general. 

Yes, but: Don't compare it with newer algorithms, as it will definitely fall short. It is computationally inefficient too. 

Stay smart: Let this idea be your next stepping stone into the world of Natural Language Processing. 

Click here to read the full tutorial

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