Vectorization

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Vectorization is the process of converting human-readable data into numbers so that machine learning models can work with it. Text can be turned into vectors using simple methods like bag-of-words or TF-IDF, or through more advanced techniques like embeddings. Images are represented by pixel values or learned features, and tabular data is turned into numeric columns that describe each characteristic of the example.

The goal is to create a fixed-length vector (or a group of vectors) that captures the important information in the data. Modern approaches often use deep learning to produce richer vectors that encode meaning and context, especially in NLP and search applications. Regardless of the technique, vectorization is a key bridge between raw data and the mathematical form that machine learning algorithms require.

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