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In todayamp;#39;s video, we are going to talk about ization in spaCy. We can do ization in NLTK as well. We have discussed the pros and cons between these two libraries and, we decided weamp;#39;ll use spaCy for the reasons I mentioned in the last video. And if you remember our NLP pipeline video, we had this uh this step called pre-processing. So in this entire NLP pipeline, weamp;#39;re going to begin with the pre-processing step. The data acquisition and text extraction and cleanup step is something we can maybe take a look at later, maybe in the end-to-end NLP project. But in pre-processing what we learned was, there is a step called sentence ization, when you you have a paragraph of text. You first separate it out in sentences and then each sentence you split it out in the into the words. So thatamp;#39;s called word ization. So we are going to see how you can do both of these things in spaCy library. Also, there was stemming, lemmetization weamp;#39;ll cover stemming, lemmet
