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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.

In this article, I will be providing you with a comprehensive definition of supervised, unsupervised and reinforcement learning in the broader field of Machine Learning. You must have encountered these terms while hovering over articles pertaining to the progress made in AI and the role played by ML in propelling this success forward. Understanding these concepts is a given fact, and should not be compromised at any cost. Here we discuss the concepts in detail, while making sure that the time you spend understanding these concepts pays off and that you are constantly aware of what is happening during this progress towards an Artificially Intelligent society.

Supervised, unsupervised and reinforcement Machine Learning basically are a description of ways in which you can let machines or algorithms loose on a data set. The machines would also be expected to learn something useful out of the process. Supervised, unsupervised and reinforcement learning lead the way into the future of machines that is expected to be bright, and will over time assist humans in doing everyday things.

Read the full article, written by Ronald van Loon.

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