Text Vectorization
Text vectorization is a natural language processing (NLP) technique that converts textual data into numerical vectors, enabling machine learning algorithms to process and analyze text. It transforms words, sentences, or documents into structured numerical representations, such as bag-of-words, TF-IDF, or word embeddings, capturing semantic and syntactic features. This process is essential for tasks like text classification, sentiment analysis, and information retrieval.
Developers should learn text vectorization when building NLP applications, such as chatbots, search engines, or recommendation systems, as it bridges the gap between human language and computational models. It is crucial for handling unstructured text data in machine learning pipelines, improving model performance by providing meaningful input features. Use cases include spam detection, topic modeling, and document similarity analysis.