# Different types of Machine learning and their types.

Prerequisite: *Introduction of Machine learning*

Supervised and unsupervised are mostly used by a lot machine learning engineers and data geeks

Reinforcement learning is really powerful and complex to apply for problems.

Supervised learning

as we know from last story machine learning takes data as input. lets call this data **Training data**

The training data includes both* Inputs* and L*abels(Targets)*

what are Inputs and Labels(Targets)?? for example addition of two numbers a=5,b=6 result =11, *Inputs *are 5,6 and *Target *is 11

We first train the model with the lots of training data(inputs&targets)

then with new data and the logic we got before we predict the output

(Note : We don’t get exact 6 as answer we may get value which is close to 6 based on training data and algorithm)

This process is called *Supervised Learning *which is really fast and accurate.

** Regression: **This is a type of problem where we need to predict the

*continuous-response*value (ex : above we predict number which can vary from -infinity to +infinity)

Some examples are

- what is the price of house in a specific city?
- what is the value of the stock?
- how many total runs can be on board in a cricket game?

etc… there are tons of things we can predict if we wish.

** Classification: **This is a type of problem where we predict

**the**

*categorical response*value where the data can be separated into specific “

**classes**” (ex: we predict one of the values in a set of values).

Some examples are :

- this mail is spam or not?
- will it rain today or not?
- is this picture a cat or not?

Basically ‘Yes/No’ type questions called **binary classification.**

Other examples are :

- this mail is spam or important or promotion?
- is this picture a cat or a dog or a tiger?

This type is called **multi-class classification.**

Here is the final picture

Classification separates the data, Regression fits the data

That’s all for supervised learning.

Unsupervised learning

The training data does not include Targets here so we don’t tell the system where to go , the system has to understand itself from the data we give.

Here training data is not structured (contains noisy data,unknown data and etc..)

ex: A random articles from different pages

There are also different types for unsupervised learning like Clustering and anomaly detection (clustering is pretty famous)

*Clustering: **This is a type of problem where we *group similar things together.

Bit similar to multi class classification but here we don’t provide the labels, the system understands from data itself and cluster the data.

Some examples are :

- given news articles,cluster into different types of news
- given a set of tweets ,cluster based on content of tweet
- given a set of images, cluster them into different objects

Unsupervised learning is bit difficult to implement and its not used as widely as supervised.

I would like to cover reinforcement learning in a separate full article as it is intense. so

That’s all for this story, Hope you get some idea.

In the next story I would like to talk about the first machine learning algorithm *Linear Regression with Gradient descent.*

See ya!