What is vectorization in machine learning?
An example of an AI chatbot conversation about vectorization in machine learning.
What is vectorization in machine learning?
Vectorization in machine learning refers to the process of converting data into a numerical format that algorithms can understand.
Why is vectorization important?
It's important because it allows for efficient computation and helps improve the performance of machine learning models.
Can you give me an example of vectorization?
Sure! For instance, in natural language processing, words can be converted into vectors using techniques like Word2Vec or TF-IDF.
How does vectorization affect model training?
Vectorization speeds up the training process by enabling parallel processing and reducing the complexity of calculations.
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