# Types of Machine Learning

There are mainly three types of Machine Learning:

* **Supervised Learning**
* **Unsupervised Learning**
* **Reinforcement Learning**

## Supervised Learning

> **"I know how to classify this data, I just need you (the classifier) to classify it"**

In this form of learning, the right answer/label is already known to the learning algorithm.

It mainly covers two types of problems:

* **Classification Problem**: **\*\*The aim is to classify a given object into** discrete\*\* class labels.

  Ex. Deciding if a person has a disease or not.
* **Regression Problem**: It deals with problems having **continuous** values for the output.

  Ex. Estimating the price of a house.

Examples of Supervised Learning: Logistic Regression, Decision Trees, Random Forests, SVMs etc.

## Unsupervised Learning

> **"I have no idea how to classify this data, can you (the algorithm) create a classifier for me?"**

In this form of learning, the right answer/label is not known in advance.

We simply have a large data set and the computer program must decide what to do with it.

It encompasses the following types of problems:

* **Clustering Problem**: In this problem, the algorithm must group the data into clusters containing similar data. Ex. Google News uses clustering algorithms to group similar news articles together.
* Some others include **dimensionality reduction** and **association rule learning**: Associative Memory, for example, could be used for a problem where a doctor must diagnose a patient based on symptoms and the past associations those symptoms had with a particular disease.

Examples of Unsupervised Learning: Apriori Algorithm, K-Means Clustering etc.

## Reinforcement Learning

> **"I have no idea how to classify this data, can you classify this data and I'll give you a reward if it's correct or I'll punish you if it's not"**

In this form of learning, the machine is trained to make specific decisions by exposing it to an environment where it trains itself continually using **trial and error**, with the aim of maximizing its reward.

Example of Reinforcement Learning: Markov Decision Process

**This course mainly focused on Supervised and Unsupervised Learning techniques.**

**The next chapter discusses a few Supervised Learning techniques.**