Tokenization

The video tutorial explains tokenization and count vectorizer, key techniques in text processing. Tokenization involves splitting text into tokens, which are small units with semantic value. An example using movie reviews illustrates this process. Count vectorizer then converts these tokens into a sparse matrix, enabling text to be transformed into numeric form for machine learning. The tutorial concludes with an application of these techniques in classification tasks, highlighting the efficiency of linear models.

What role do machine learning algorithms play after transforming text into numeric form?

Why is it often sufficient to use linear models for classification in this context?