In the previous chapter, we saw how to learn from data when the labels or true values associated with them are available. In other words, we knew what was right or wrong and we used that information to build a regression or classification model that could then make predictions for new data. Such a process fell under supervised learning. Now, we will consider the other big area of machine learning where we do not know true labels or values with the given data, and yet we will want to learn the underlying structure of that data and be able to explain it. This is called unsupervised learning.
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