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In this video, we will talk about TF-IDF representation for text in NLP. In the last video we looked at Bag of words, Bag of n-grams. If you have not seeing those videos, I highly recommend you watch them first before watching this video. We have been looking at news article classification problem, where a news article is given to you, and you have to classify it into one of the companies, whether this article is for Tesla or Apple, etc. And in Bag of words model what we did was weamp;#39;ll take an article and then design this vocabulary. Doc, vocabulary is basically all the words from all the news articles and then we will take count of individual word in a given article. For example in article 1, word Musk is present 0 time, that is present 32 times and so on. And just by looking at this vector right now, you can actually tell that first 2 articles are Apple articles because you see these terms, iPhone, itunes they appear 7 times, 3 times, 6 times, 3 times. Whereas in the other 2 a