Day3 — Machine Learning and its types
By: Shridhar Hegde
Tags: Machine Learning, Supervised Learning, Reinforcement Learning, Unsupervised Learning, Artificial Intelligence

Dramatic illustration of Machine Learning

This is the blog for the third day of my internship.

Today’s task is to learn about three different types of Machine Learning techniques - Supervised, Unsupervised and Reinforcement Learning. So let’s dive right in.

What is Machine Learning ?

Before getting to know the types of Machine Learning, let us see the definition of Machine Learning.

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.

So in this era, data is the key. Machine Learning uses tons of data to learn some similarities and patterns in data to make predictions about the random data point that is encountered in the future.

Types of Machine Learning

As seen from the picture which is at the top of this article, Machine Learning has three basic types. They are:

Supervised Learning Unsupervised Learning Reinforcement Learning

Illustration of three different types of Machine Learning

Let us see each one of these ML types in detail. Keep in mind that these classifications are based on the type of input data.

Supervised Learning

Supervised Machine Learning algorithms use Labelled Data as input. There is a prior knowledge about where the data is obtained from and what does it represent.

For example, look at the graph below of no of hours of study done by a student vs the percentage marks scored. Here, if we give an input say 6 hours of study, the Ml algorithm can predict what can be the percentage marks of the student. The data that is given has labels.

Supervised Data of hours vs percentage plot

This is the scenario of Supervised Learning.

Unsupervised Learning

When the input data is not labelled and we do not have any prior knowledge about the data being given, it is called as Unsupervised Learning.

Look at the below image.

Illustration of Unsupervised Learning

The inputs here are some vegetables. Assume that we don’t know the names of these vegetables. What we can do then is that group all the similar looking vegetables into one group which is shown in the right hand side of this image. Given a new vegetable, you can take it and put it into its similar looking group.

Reinforcement Learning

In this type of learning we take suitable action to maximize reward in a particular situation. In Supervised and Unsupervised learning, the answer is embedded in data point that is given. But in Reinforcement Learning that is not.

This learning method goes by using rewards. If something wrong happens, negative reward is given and if something good happens, positive reward is given. By doing this, we are training the machine to get good results (here good result means the results what we are expecting).

Take an example of a student. If the student gets say 90% in exam, parents give him chocolate or take him for a trip. If the student gets less than their expectations, they scold the child. This makes the child think that he will get good reward if he/she studies well. This is a similar scenario of reinforcement learning.

So this was the today's learning. Hope you find it useful. Thanks for reading.