Can you explain how vectorization works in machine learning?
Sure! Vectorization is the process of converting data into numerical vectors, which allows algorithms to process it efficiently.
Why is vectorization important for machine learning models?
Vectorization is crucial because it enables models to perform mathematical operations on data, improving speed and performance.
What are some common techniques used for vectorization?
Common techniques include one-hot encoding, TF-IDF for text data, and word embeddings like Word2Vec or GloVe.
Can you give an example of vectorization in natural language processing?
Certainly! In NLP, vectorization can transform sentences into numerical arrays using techniques like word embeddings, allowing models to understand context.
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