Embedding in machine learning refers to the process of converting high-dimensional data into a lower-dimensional space, making it easier to analyse and interpret. This technique is particularly crucial in the field of natural language processing (NLP), where words are transformed into vectors in a continuous vector space, preserving semantic relationships. By utilising embeddings, machine learning models can effectively handle and process large datasets, improving their performance and accuracy.

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