In these few videos, we'll take a look at the tokenizers. In Natural Language Processing, most of the data that we handle consists of raw text. However, machine learning models cannot read and understand text in its raw form they can only work with numbers. The tokenizer's objective will be to translate the text into numbers. There are several possible approaches to this conversion, and the objective is to find the most meaningful representation. We'll take a look at three distinct tokenization algorithms. We compare them one to one, so we recommend you look at the videos in the following order: Word-based, Character-based, and Subword-based.