Supervised learning algorithms are used when the output is classified or labeled.
This algorithms learn from the past data that is inputted, called training data, runs its analysis and uses this analysis to predict future events of any new data within the known classifications.
Unsupervised learning is a machine learning technique in which the users do not need to supervise the model. Instead, it allows the model to work on its own to discover patterns and information that was preciously undetected.
It mainly deals with the unlabelled data.
This type of machine learning algorithm uses the trial and error method to churn out output based on the highest efficiency of the function.
The output is compared to find out errors and feedback, which are fed back to the system to improve or maximize the performance.