2. Types of Machine Learning

Supervised Learning

In Supervised Learning, input is provided as a labelled dataset. We build a model that can learn a function to perform input to output mapping. There are 2 types of Supervised Learning problems.

In this type, the model predicts a discrete value. The input data is usually a member of a particular class or group. For example, predicting whether the given image if of a dog or not.

These problems are used for continuous data. For example, predicting the price of a piece of land in a city, given the area, location, number of rooms, etc.

This learning algorithm is completely opposite to Supervised Learning. In short, there is no complete and clean labelled dataset in unsupervised learning. Unsupervised learning is self-organized learning. Its main aim is to explore the underlying patterns and predicts the output. Here we basically provide the machine with data and ask to look for hidden features and cluster the data in a way that makes sense.

It is neither based on supervised learning nor unsupervised learning. Moreover, here the algorithms learn to react to an environment on their own. It is rapidly growing and moreover producing a variety of learning algorithms. These algorithms are useful in the field of Robotics, Gaming etc.
For a learning agent, there is always a start state and an end state. However, to reach the end state, there might be a different path. In Reinforcement Learning Problem an agent tries to manipulate the environment. The agent travels from one state to another. The agent gets the reward(appreciation) on success but will not receive any reward or appreciation on failure. In this way, the agent learns from the environment.

NOTE: WE WILL NOT BE DISCUSSING REINFORCEMENT LEARNING