Supervised Learning: As we know we have to train our machine before expecting anything sensible from them in ML. Supervised machine learning algorithm is like a teacher teaching a student a certain subject. Teacher first tells the student what is right and what is wrong. They even check their test papers. Students have the answers to their question. After being trained to a certain degree, teacher expects them to be right.
This is how supervised machine learning algorithms work. Algorithm learns from data set and correct and wrong results.
Unsupervised Learning : Under unsupervised learning there is no teacher and no student. It’s like self study. Algorithm has to learn from the data set , how and on what basis it read the data set and make sense of it. For example, we can put a bucket full of fruits in machine and expect it to make sense of all the different fruits in it on the basis of shape, size, color and structure.
Reinforcement Learning : Under reinforcement learning algorithms, machines learn from their progress. Under this type algorithms determine what the ideal behavior within a context can be. In Reinforcement Learning an agent decides the best action based on the current state of the results. Below graphics explains a lot about the reinforcement learning algorithms working.
Note : This post is under progress and will be updated with time.
