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.
Data Preprocessing involves preparing and cleaning raw data before analysis. By removing noise, selecting relevant features, and addressing missing values, businesses can ensure data…