Word2Vec

Word2Vec is a machine learning technique that transforms words into numerical vectors, capturing their meanings based on context. It analyzes large amounts of text to understand which words often appear together or in similar situations. By doing so, it creates a mathematical space where related words are positioned closer together, enabling computers to recognize relationships and similarities between words—like "king" and "queen" or "apple" and "banana." This helps in various natural language tasks, such as translation, search, and sentiment analysis, by providing a meaningful way for machines to interpret language patterns.