What is the difference between vectorization and normalization?
An example of an AI chatbot conversation about vectorization and normalization in data processing.
Can you explain the difference between vectorization and normalization?
Sure! Vectorization refers to the process of converting data into a vector format, while normalization is about scaling data to a specific range.
Why is vectorization important in machine learning?
Vectorization is crucial because it allows algorithms to process data more efficiently, improving performance and speed.
And what about normalization? When should I use it?
Normalization is used to ensure that different features contribute equally to the distance calculations, especially in algorithms like k-NN.
Can you give me an example of when to use both?
Certainly! In image processing, you might vectorize pixel values and then normalize them to ensure consistent brightness across images.
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