What is the term for the process of converting text data into numerical form for machine learning?

The correct answer is: D. Text vectorization.

Text vectorization is the process of converting text data into a numerical representation that can be used by machine learning algorithms. This is done by converting each word in the text into a unique numerical ID, called a token. The tokens are then arranged into a vector, with each position in the vector representing a different token.

Text vectorization is a necessary step for many machine learning tasks, such as text classification and sentiment analysis. It allows machine learning algorithms to process text data in a way that they can understand.

Here is a brief explanation of each option:

• Feature scaling is the process of normalizing the values of features in a dataset so that they have a similar scale. This is done to prevent the values of features from having an undue influence on the results of machine learning algorithms.
• One-hot encoding is a technique for representing categorical data as a vector of binary values. This is done by creating a separate binary feature for each possible category.
• Data normalization is the process of transforming data so that it has a normal distribution. This is done to improve the performance of many machine learning algorithms.

I hope this helps!