Types of Machine Learning problems

Types of Machine Learning problems:

1. Supervised Learning
2. Un-Supervised Learning
3. Reinforcement Learning

1. Supervised Learning: Here, we want to make certain predictions for the future. Hence, we want the machine to learn the previous historical data and forecast for the future for instance, temperature for today. We provide both the input value and output (labels) of historical data such as climate details, humidity etc and along with that output like temperature. Hence, the model can find the derivatives between input and output and generate an equation. eg regression and classification

2.Unsupervised Learning : Un-Supervised Learning is to simply find out different patterns in data and categorize something or group something or segment something. Here, we only provide input values not output values. We will only provide the relative features of the class but will not label them. For instance, for identifying a dog, we give features such as long tail, sharp teeth, sharp claws, makes a boow noise etc. But we will not give the label for it. We only expect the machine to make a segregation based on the underlying features. Therefore, Unsupervised Learning does not make any predictions for the future but only makes segregations.

3. Reinforcement Learning: it is known as reward-based learning. For instance, we have a robot that is learning how to walk. We train the robot to walk straight and if it bang anything on the way like wall or table etc, then it should turns left or right and move forward or it takes an about turn and come back. Each time it bangs somewhere, we would point out his mistake and tell him where he is going wrong. Reward him in such a way that if he does not bang anywhere, we provide a rewarded system where the robot is rewarded (appreciated for his good work), and the robot does not commit the same mistake again. So this is what Reinforced Learning is all about.

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