If you are venturing into machine learning, you should know about supervised and unsupervised machine learning. People often find it difficult to draw a line of difference between these two. Apparently, both the learning processes use the same procedure. This further makes it complicated for the learner to differentiate between supervised and unsupervised machine learning. Here, you will come to know the differences between these two types of machine learning.
The one point that I want to emphasize here is that the adjective "unsupervised" does not mean that these algorithms run by themselves without human supervision. It simply indicates the absence of a desired or ideal output corresponding to each input. An analyst (or a data scientist) who is training an unsupervised learning model has to exercise a similar kind of modeling discipline as does the one who is training a supervised model. Alternatively, an analyst who is training an unsupervised learning model can exercise a similar amount of control on the resulting output by configuring model parameters as does the one who is training a supervised model. While supervised algorithms derive a mapping function from x to y so as to accurately estimate the y's corresponding to new x's, unsupervised algorithms employ predefined distance/similarity functions to map the distribution of input x's.
Once we start delving into the concepts behind Artificial Intelligence (AI) and Machine Learning (ML), we come across copious amounts of jargon related to this field of study. Understanding this jargon and how it can have an impact on the study related to ML goes a long way in comprehending the study that has been conducted by researchers and data scientists to get AI to the state it now is.