Difference between SUPERVISED and UNSUPERVISED LEARNING in Machine Learning
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According to Wikipedia, Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" with data, without being explicitly programmed.
This simply means that "Machine Learning" is an ability through which a computer 'learn' by its own.
Machine learning can be broadly classified into two classification-
1)Supervised learning
And
2)Unsupervised learning
And today in this article, I will show what does it mean by "supervised" and 'unsupervised learning'
WHAT IS THE DIFFERENCE????
The primary difference between supervised learning and unsupervised learning is the data used in either method of machine learning.
In Supervised Learning, the data provided has a known output. Supervised LEARNING involves allocating labelled data so that a certain pattern or function can be deduced from that.
Let's say you are making a programme which finds the colour of a flower, if it's blue or red. Since we have only two outputs to our data, which is either red or either blue, the type data which will get in the output is already known.
Simply speaking, a SUPERVISED data model has an input as well as an output.
Unsupervised Learning is just the opposite of Supervised Learning.
In Supervised LEARNING, we have an input and a known output.
But in UNSUPERVISED LEARNING, we only have the input, not the known output. The goal of unsupervised learning is to determine the 'hidden patterns or grouping of data' from unlabeled data. Unsupervised Learning is used in making Robots or A-I because a robot might have different inputs but nobody really knows what a output the robot would give back.
Supervised Learning is mostly used in the field of Machine Learning as it takes LESS ALGORITHMS, Less computer ram, Less number of hardware and is Pretty Easy to Learn.
However, Unsupervised Learning needs a Tons of COMPLEX ALGORITHMS, Takes more computer ram as well as storage, Takes High Level performingh processor and it is Really Hard to master it.
SO WHAT SHOULD I TAKE???????
Well, if you have a good hardware (like very good processor and ram) and lots of spare time to learn, I definitely suggest Unsupervised Learning. Once you master it, you can make the complex and complex of stuff around it like, making a robot, or developing a programme which can make its own decision, and best of all, the demand of unsupervised machine learning experts is really high on demand and have a great income of about $120,000 approximately.
On the other hand, if you lack in great hardware and have limited time to spare, SUPERVISED LEARNING is what I suggest. Due to the fast and easy learning curve, you can quickly learn supervised learning. An average data scientist who excel in SUPERVISED LEARNING earn up to $67,000 per annum.
SO HOW TO GET STARTED?
To get started with machine learning, you should learn a couple of subjects in maths which includes:-
1)Calculus
2)Trigonometry
3)Linear Regression
and
4)Basic maths (of course).
I would also recommend learning how the stock market works because it is the most common place where machine learning scientist is really required.
Apart from that, I really suggest that you should also learn a main Programming language.
The top programming language I prefer is:-
1)Python
And
2) R programming language
I highly recommend starting with Python as python is far most the BEST programming language to work on.
It has a really easy learning curve, best for the beginners who have no knowledge of programming language, very simple yet very powerful.
Python is used largely in Data Science, A-I and Web Development. It is also used for secondary purposes like making games, apps, GUI development etc.
I have made a complete blog on why I prefer the python language the most - check it out if you want more details
So that's it.
Hope you liked and enjoyed reading my article,
Comment down below if you want to share your opinion on my article
And I will see you in next time
 |
This simply means that "Machine Learning" is an ability through which a computer 'learn' by its own.
Machine learning can be broadly classified into two classification-
1)Supervised learning
And
2)Unsupervised learning
And today in this article, I will show what does it mean by "supervised" and 'unsupervised learning'
WHAT IS THE DIFFERENCE????
 |
In Supervised Learning, the data provided has a known output. Supervised LEARNING involves allocating labelled data so that a certain pattern or function can be deduced from that.
Let's say you are making a programme which finds the colour of a flower, if it's blue or red. Since we have only two outputs to our data, which is either red or either blue, the type data which will get in the output is already known.
Simply speaking, a SUPERVISED data model has an input as well as an output.
Unsupervised Learning is just the opposite of Supervised Learning.
In Supervised LEARNING, we have an input and a known output.
But in UNSUPERVISED LEARNING, we only have the input, not the known output. The goal of unsupervised learning is to determine the 'hidden patterns or grouping of data' from unlabeled data. Unsupervised Learning is used in making Robots or A-I because a robot might have different inputs but nobody really knows what a output the robot would give back.
Supervised Learning is mostly used in the field of Machine Learning as it takes LESS ALGORITHMS, Less computer ram, Less number of hardware and is Pretty Easy to Learn.
However, Unsupervised Learning needs a Tons of COMPLEX ALGORITHMS, Takes more computer ram as well as storage, Takes High Level performingh processor and it is Really Hard to master it.
SO WHAT SHOULD I TAKE???????
Well, if you have a good hardware (like very good processor and ram) and lots of spare time to learn, I definitely suggest Unsupervised Learning. Once you master it, you can make the complex and complex of stuff around it like, making a robot, or developing a programme which can make its own decision, and best of all, the demand of unsupervised machine learning experts is really high on demand and have a great income of about $120,000 approximately.
On the other hand, if you lack in great hardware and have limited time to spare, SUPERVISED LEARNING is what I suggest. Due to the fast and easy learning curve, you can quickly learn supervised learning. An average data scientist who excel in SUPERVISED LEARNING earn up to $67,000 per annum.
SO HOW TO GET STARTED?
 |
1)Calculus
2)Trigonometry
3)Linear Regression
and
4)Basic maths (of course).
I would also recommend learning how the stock market works because it is the most common place where machine learning scientist is really required.
Apart from that, I really suggest that you should also learn a main Programming language.
The top programming language I prefer is:-
1)Python
And
2) R programming language
I highly recommend starting with Python as python is far most the BEST programming language to work on.
It has a really easy learning curve, best for the beginners who have no knowledge of programming language, very simple yet very powerful.
Python is used largely in Data Science, A-I and Web Development. It is also used for secondary purposes like making games, apps, GUI development etc.
I have made a complete blog on why I prefer the python language the most - check it out if you want more details
So that's it.
Hope you liked and enjoyed reading my article,
Comment down below if you want to share your opinion on my article
And I will see you in next time
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