Word Embeddings are a technique in NLP that represent words as continuous vectors in a high-dimensional space. These vectors capture semantic and syntactic relationships between words. Word embeddings are useful for tasks such as language translation, sentiment analysis, and document clustering.
Supervised Learning is a Machine Learning approach where models are trained using labelled data, with both input and output pairs. By learning from the…