Most of the data that you will encounter out in the wild will not come with labels. It is impossible to apply supervised machine learning techniques when your data does not come with labels. Unsupervised machine learning addresses this issue by grouping data into clusters; we can then assign labels based on those clusters.
Once the data has been clustered into a specific number of groups, we can proceed to give those groups labels. Unsupervised machine learning is the first step that you, as the data scientist, will have to implement, before you can apply supervised machine learning techniques (such as classification) to make meaningful predictions.
A common application of the unsupervised machine learning algorithm is customer data, which can be found across a wide range of industries. As a data scientist, your job is to find...
