TEXT AND EMBEDDED VECTORIZATION IN MACHINE LEARNING

Text and Embedded Vectorization in Machine Learning are extremely confused with general Data or Text Vectorization.

Conceptually, both do the same that is to speed-up the task.

General Vectorization is to convert single action on single data element (SISD) to single action to multiple data elements.

Text Vectorization in Machine Learning converts text or characters into numbers then Embedding process refers to those converted numbers for learning using machine and deep learning to produce faster results.

We won’t be going in details but some of the most used Text Vectorization in Machine Learning methods are Binary Term Frequency, Bag of Words (BoW) Term Frequency, Normalized Term Frequency etc.

Cheers.