Supervised Learning vs Unsupervised Learning

supervised and unsupervised learning algorithms are the two main kinds of machine learning algorithm.
Supervised learning is the learning algorithms where data is labeled and in UnSupervised learning, data is not labeled. Here we discussed detailed supervised and unsupervised learning.


Supervised learning 

                            The major part of practical machine learning uses the supervised learning.
In supervised learning, we have an input variable X and output variable Y and we use an algorithm
to learn the mapping function from the input and output.
                                
                                                                Y =  X +c
here 
                  X  =   input variable                     ( training data sets)
                  Y  =   output  variable                  ( target data)

the goal is to approximate the mapping function so when you have new input data (x) that you can predict the output variables (Y) for that data.

if you are training your machine learning task for every input with the corresponding target is known as supervised learning.

   here we start with a simple example: in supervised learning, we already know something about data ( we know the features) mean given data have label .we  already know something about data 

Supervised learning   real-world example:

  • suppose you had a basket and it is filled with some fresh fruits your task is to arrange the same type of fruits at one place.
  • suppose the fruits are apple, banana, cherry, grapes.
  • so you already know from your previous work(  knowledge) that, the shape of each and every fruit so it is easy to arrange the same type of fruits in one place.
  • here your previous work is called as train data.
  • so you already learn the things from your train data, This is because of you have a response variable which says you that if some fruit has so and so featured it is grape, like that for each and every fruit.
  • This type of data you will get from the train data.
  • This type of learning is called supervised learning.
  • This type solving problem come under Classification.
  • So you already learn the things so you can do your job correctly.


Supervised learning problems can be further grouped into regression and classification problems.
  • Classification: A classification problem is when the output variable is a category, such as “red” or “black” or “no disease” and “disease”.

  • Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
Some common types of problems built on top of classification and regression include recommendation and time series prediction respectively.

Unsupervised learning  :

   In unsupervised learning you have only input data X there is no corresponding output variable. In unsupervised learning, the data is not labeled so we cannot find the correct answer.

With unsupervised algorithms, you still don’t know what you want to get out of the model yet. You probably suspect that there have to be some kinds of relationships or correlation between the data you have, but data is too complex to try to guess.so use the data normalization process so you can easily compare.

the additional importance of this model is that the model can suggest different ways to categorize or order your data.

Unsupervised learning, on the other hand, allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don't necessarily know the effect of the variables.
We can derive this structure by clustering the data based on relationships among the variables in the data.
With unsupervised learning, there is no feedback based on the prediction results, 


example of unsupervised learning :

  • suppose you had a basket and it is filled with some fresh fruits your task is to arrange the same type of fruits at one place.
  • This time you don't know anything about that fruits, you are the first time seeing these fruits so how will you arrange the same type of fruits.
  • What you will do first you take on fruit and you will select any physical characteristics of that particular fruit. suppose you taken color.
  • Then you will arrange them base on the color, then the groups will be something like this.
  • RED COLOR GROUP: apples & cherry fruits.
  • GREEN COLOR GROUP: bananas & grapes.
  • so now you will take another physical character as size, so now the groups will be something like this.
  • RED COLOR AND BIG SIZE: apple.
  • RED COLOR AND SMALL SIZE: cherry fruits.
  • GREEN COLOR AND BIG SIZE: bananas.
  • GREEN COLOR AND SMALL SIZE: grapes.
  • a job is done the happy ending.
  • here you didn't know to learn anything before means no train data and no response variable.
  • This type of learning is known as unsupervised learning.


 if you guys have any question about Supervise learning or un-supervised learning ask in the comment section. I will try best.