Embedding is a technique used in machine learning and natural language processing to represent words, phrases, or even entire documents as numerical vectors. This allows computers to understand and process text data more effectively by capturing semantic relationships. For example, words with similar meanings are represented by vectors that are close together in a multi-dimensional space. In addition to text, embedding can also be applied to images and other types of data. Techniques like Word2Vec and BERT are popular for text embeddings, while Convolutional Neural Networks (CNNs) are often used for image embeddings. These methods help improve the performance of various applications, such as search engines and recommendation systems.