Hello:
People, I have an open question could someone compare the benefits and costs to use the ML + sigmoid function versus K-means, to classification?
I noticed that the course is concentrated in the sigmoid solution.
Maybe I’m asking an stupid question. (Most likely tststs).
Did I miss something? Thank you.
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What do you mean here by ‘ML’ ?
I mean “Machine Learning”.
Oh, yes I know you meant that. It is just that Machine Learning is a subject matter, not an algorithm. Whereas K-means or KNN is.
Do you mean like a small neural network ? Or I am guessing our trying to understand because you mention sigmoid (an activation function).
And your question isn’t stupid, just trying to understand what is being asked.
K-means isn’t used for classification. It’s an unsupervised method, which is used for clustering.
K-means is discussed in MLS Course 3.
Ahhhh Got it. In my mind… “categorization” and “Clustering” were the same.
Mistery solved. Thank-you for the explanation.
