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In unsupervised machine learning, there is no answer given. Like in my story here, the red dot and blue dot are not given to the machine, the color is assigned after the machine runs its model.

Supervised machine learning means that for every set of data, we assign a 'true answer' to the model. Then the model will try to work out its way to generate some parameters (for example a regression) to predict the next answer by another set of data.

I got it now, very clear explanation, thanks!