Machine Learning is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own without being explicitly programmed.

Supervised means to oversee or direct a certain activity and make sure it is done correctly. In this type of learning the machine learns under guidance. So, at school or teachers guided us and taught us similarly in supervised learning machines learn by feeding them label data and explicitly telling them that this is the input and this is exactly how the output must look. So, the teacher, in this case, is the training data.

Unsupervised means to act without anyone’s supervision or without anybody’s direction. Now, here the data is not labeled. There is no guide and the machine has to figure out the data set given and it has to find hidden patterns in order to make predictions about the output. An example of unsupervised learning is an adult-like you and me. We do not need a guide to help us with our daily activities. We can figure things out on our own without any supervision.

Reinforcement means to establish or encourage a pattern of behavior. Let us say that you were dropped off at an isolated island. What would you do? Now initially you would panic and you would be unsure of what to do where to get food from how to live and so on. But after a while you will have to adapt you must learn how to live on the island. Adapt to the changing climates learn more to eat and what not to eat. So here you are basically following the hit and trial concept because you are new to the surroundings and the only way to learn is experience and then learn from your experience. This is what reinforcement learning is. It is a learning method wherein an agent, which is basically you stuck on the island interacts with its environment which is the island by producing actions and discovers errors or rewards. Once the agent gets trained it gets ready to predict the new data presented to it.

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