Having processed raw text data, the next logical step is converting it into a numerical format that machine learning algorithms can understand. Raw text strings are not directly suitable for most models, necessitating the creation of meaningful numerical features.

This chapter introduces fundamental feature engineering techniques for text representation. We will start with the intuition behind simple count-based methods like Bag-of-Words and progress to the widely used Term Frequency-Inverse Document Frequency (TF-IDF) weighting scheme. You will learn how TF-IDF scores, often calculated as $TF \times IDF$, help quantify the importance of words within a collection of documents.

We will also examine how N-grams (bi-grams, tri-grams, etc.) can be used to incorporate local word order and context into your features. Finally, we'll touch upon methods for handling the potentially high dimensionality of text features, including feature hashing and dimensionality reduction techniques like Singular Value Decomposition (SVD). Upon completing this chapter, you will be equipped to generate and compare different numerical representations for text data, preparing it for input into machine learning models.