Machine Learning Resources

Differentiate between Supervised vs. Unsupervised Learning

  • Supervised Learning: In a supervised learning problem, there is an explicit, pre-defined target variable which has known labels. If the outcome is continuous, the problem is solved via a regression context. If the outcome has discrete categories, a classification approach is taken. 
  • Unsupervised Learning: On the other hand, unsupervised learning is performed when the target labels are unknown. Thus, there is no explicit mapping from the feature space X to the target Y. In some cases, the target is unable to be measured precisely, or it can be unknown entirely. A common unsupervised learning task is clustering observations into categories based on their feature similarities and then deriving and interpreting class labels based on the shared feature values of each grouping.  

Find out all the ways
that you can