I have been reading about the Supervised and Unsupervised learning. What I came to know through this link is that in case of Supervised learning you have a set of input and a set of labels which are then used in the training phase whereas in case Unsupervised learning there are no labels. Now my basic question is:
Since there are no labels available in case of Unsupervised learning so does that mean that there is no training phase in unsupervised learning?
If there is a training phase in Unsupervised learning and we do not use any labels then what is the importance of training phase and how is it performed since we do not have any error or reward signal to evaluate a potential solution in training phase? Isn't it equal to the normal testing phase where we have the set of data but no labels and we predict the labels?
I was reading about a Nonparametric bayesian modeling method and the introduction said that-"The method is unsupervised and has no free parameter that require tuning". In this what is the meaning of no free parameter that require tuning? Is it that there is no requirement of training phase?
Please bear with my lack of understanding about these topics as I am new to this and would love if you could provide some explanation to clear out my doubts.