What is Tokenization? Types, Use Cases, Implementation | DataCamp

DataCamp and our partners use cookies to improve your learning experience, offer content relevant to your interests and show more relevant advertisements. You can change your mind at any time (learn more & configure). Tokenization, in the realm of Natural Language Processing (NLP) and machine learning, refers to the process of converting a sequence of text into smaller parts, known as tokens. These tokens can be as small as characters or as long as words. The primary reason this process matters is that it helps machines understand human language by breaking it down into bite-sized pieces, which are easier to analyze. Imagine you're trying to teach a child to read. Instead of diving straight into complex paragraphs, you'd start by introducing them to individual letters, then syllables, and finally, whole words. In a similar vein, tokenization breaks down vast stretches of text into more digestible and understandable units for machines. The primary goal of tokenization is to represent te

DataCamp and our partners use cookies to improve your learning experience, offer content relevant to your interests and show more relevant advertisements. You can change your mind at any time (learn more & configure). Tokenization, in the realm of Natural Language Processing (NLP) and machine learning, refers to the process of converting a sequence of text into smaller parts, known as tokens. These tokens can be as small as characters or as long as words. The primary reason this process matters is that it helps machines understand human language by breaking it down into bite-sized pieces, whic