We hope you have understood about machine learning and if you have not gone through the post on machine learning, kindly
As the name goes supervised learning is just like the learning process used in schools, where there are a set of instructions given by our teacher and we are asked to perform a certain specified task. But, in case of machine learning the training data set is provided to the computers, and the user acts as a teacher where the algorithms are designed to give a very specific output (just as the students).
The machines are trained on the predefined data set before it can be used to make any decisions based on new data. For example, the machines are set to identify certain objects based on very specific characteristics which are very unique to that particular object.
Suppose a machine is trained on identifying whether a particular creature is a dog or cat. Specific features of dog and cat are fed to the computer during the training process and levelling of the pictures are done so that whenever new characteristics or features are fed into the computer it is able to correctly identify those features and come up with the outcome of whether it is a dog or cat.
The second type of learning is unsupervised learning where the machines are given a particular data set and left to learn on its own. To simplify, the machine is fed with data and the algorithms by itself tries to find pattern and features in the data set and put them into different categories by identifying patterns and correlation within the units of the data set.
It can be mostly said as a self-study process, where the machine learning algorithm is put into its own to identify and act by identifying relationship among the features of the different clusters. Clustering is one of the most commonly used unsupervised machine-learning techniques.
The Third category of machine learning is reinforcement learning, where the machines are left to learn by itself, but, here the algorithms of learning are rewarded or penalized based on the actions that they perform. For example as a child if you do good things you are rewarded, and hence, you are encouraged to perform that action again and again, whereas if you are given punishment for a particular act of yours you tend to not repeat it.
Similarly in reinforcement learning the algorithm is rewarded when it performs the correct task and thus the algorithm learns and tries to repeat the same task again and again. Whereas if penalized for a task it tries not to repeat those task.
In this reinforcement learning process, the machine learns by the trial and error method. In terms of machine learning that rewards and penalties are decided based on the predefined actions which the machine needs to perform in interaction with its environment.
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