NLP Text preprocessing and tokenization.

Welcome to our NLP lesson on tokenization and preprocessing! In this video, we'll explore the importance of tokenization in NLP and how it helps to create a structure that can be easily processed by computers. We'll also delve into the next step of preprocessing and how it further refines the tokenized text to improve its accuracy and usefulness for subsequent analysis.

We'll cover key preprocessing techniques such as lowercasing, stemming/lemmatization, stopword removal, and normalization. These techniques play a critical role in preparing text data for NLP and machine learning models, and can be used for a variety of applications, from sentiment analysis to language translation.

This video is perfect for anyone interested in NLP and machine learning, whether you're a beginner or an experienced practitioner. By the end of the video, you'll have a better understanding of tokenization and preprocessing, and how they are used in NLP applications. So sit back, relax, and let's dive into the fascinating world of NLP!