SUPERVISED VS. UNSUPERVISED MACHINE LEARNING
Data scientists use many different kinds of machine learning algorithms to discover patterns in big data that lead to actionable insights. At a high level, these different algorithms can be classified into two groups based on the way they “learn” about data to make predictions: supervised and unsupervised learning.
In supervised machine learning, the algorithm is provided with a set of examples such that each example has a label (e.g., whether an email is spam) and the algorithm attempts to “figure out” how to map these examples to their corresponding labels. This process is similar to human cognitive heuristics, but the primary difference between this and human learning is that machine learning runs on computer hardware and is best understood through the lens of computer science and statistics, whereas human pattern-matching happens in a biological brain (while accomplishing the same goals).
SUPERVISED LEARNING IN ACTION
Siri, Alexa, Cortana or any speech automated system in your mobile phone trains your voice and then starts working based on this training. This is an application of Supervised Learning
Understanding Unsupervised Learning
Mathematically, Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data