Why unsupervised machine learning is important?

why unsupervised machine learning is important, difference between supervised and unsupervised learning, advantages and disadvantages of unsupervised

UNSUPERVISED MACHINE LEARNING

In this blog, we will discuss what is unsupervised machine learning and how does it work? What are the examples of it and why it is so important?

Machine Learning

Machine learning is teaching your machine about something. That means it also deals with collecting and cleaning data, creating several algorithms. Then teaching the algorithm essential pattern for respective data, and then we expect from the algorithm that it will give us the output. Machine learning is the process of creating a model. Furthermore, it can perform a certain task without the need for human explicit programming to do.

Three basic types of machine learning. These are supervised, reinforcement, and unsupervised learning.

In the supervised type, the learning process means the collected data is labeled data and we train the machine. So this helps you to correct your algorithm if it makes any mistake in giving your answer which means you have a mapped output also. That we have done in the test set of data and trained set of data

Reinforcement there is no data in the kind of learning nor do you teach the algorithm. Your model or the algorithm such as it interacts with the environment and does its job.

In unsupervised learning, the data is collected and has no labels that mean we are not sure. So as it has no labels that mean we are unsure about the outputs. Also, the model of your algorithm can understand patterns of data and the output is the required answer. We do not interfere when the algorithm learns that means there is no interruption by a human. So the machine itself learns the data is known as unsupervised learning.

Few examples of unsupervised learning

A common example is a student is learning by himself. A student is learning by analyzing the thing and resolving their issues itself that can be said as self-learning. Where the algorithm can before unknown patterns of the data set. That means it can find the unknown pattern of the data set. There is no label data that the student is learning from any of the websites available for learning.

Another example of unsupervised machine learning could be a football match. For example, you never watch football. But you analyze it once you start watching by looking at the scoreboard and goal strikes. That can be an example of unsupervised learning where earlier you are blank and do not have any concept of labeled data. But still, analyze things and predicting the result.

Why unsupervised machine learning is important?

The reason why we need unsupervised machine learning is that these learning algorithms work on data set that are unlabeled and find patterns that would before not be known to us. That means it is working on a data set that is unlabeled and after resulting it could find certain patterns which might be earlier not known to us. For finding those particular patterns we need the concept of unsupervised machine learning. These pattern options are helpful if we need to categorize the element or to find an association between them unsupervised learning is then important. We can also help to detect anomalies and defects in the data set which need to be pruned or removed. The data which we collect is unlabeled which makes work easy when we use these algorithms. It is not necessary for all the time that we will have categorized data or label data. Most of the time we use unlabeled data then the unsupervised machine learning algorithms come in.

What is the basic difference between supervised learning and unsupervised learning?

In supervised learning a trainer or let's say teacher teaches the student he trains the two students, several models, in such a way that they can respond to the corresponding inputs. Unsupervised learning is such a format in which the student learns by himself. That means no labeled set of data is had or we can say that he or she doesn't have any respective output which is needed to be given. He classifies several algorithms and learns. That is unsupervised learning.

Some more differences are there in supervised and unsupervised machine learning.

The first difference that is in supervised learning we have algorithms and are trained using the labeled data. Whereas in unsupervised learning the algorithms are trained using unlabeled data.

In supervised learning, the model takes direct feedback to check that if it is predicting correct input according to the output that means the correct prediction is done or not. Whereas in unsupervised learning model does not take any feedback.

A supervised learning model predicts the output. Whereas the unsupervised learning model finds the hidden pattern of data.

One of the major differences is that in supervised learning input data is provided to the model along with the output. That means input is also provided and output data is also provided which we classify and fit the model according to the X strain, Y strain, and X test, Y test.

But in unsupervised only the input data is provided to the machine no output data is provided.

Supervised learning can be categorized into several classifications and regression problems. Whereas unsupervised learning can be classified into clustering and association in these two things it can be classified.

Now supervised learning model produces an accurate result. Whereas unsupervised learning can use those cases where we have only input data and then it corresponds to the output. So that means it gives less accurate results. So this is about the supervised and unsupervised machine learning differences.

Also in supervised machine learning, the linear regression, the logistic regression, and the decision tree. So all these are various algorithms that are performed using supervised machine learning. Whereas unsupervised machine learning has clustering, KNN, and a priory algorithm. So it consists of them. So these are the basic differences that can be classified among supervised and unsupervised machine learning.

The advantages of unsupervised machine learning

Unsupervised learning is used for more complex tasks as compared to supervised learning. Because in unsupervised learning we do not have the labeled input data. It cannot be possible to get the proper label data along with the respective output. So that's why it does more complex tasks.

Unsupervised learning is easy to get unlabeled data. In comparison to the label data which is quite the same as the other thing.

The disadvantages of unsupervised learning

The unlabeled data brings up the disadvantages of unsupervised machine learning. Because it is much difficult to process. As in supervised learning it is done not a lot the corresponding output in supervised learning we have a corresponding output and according to that, we train our algorithm in the machine. So that it gives the respective output. But that is not possible with unsupervised learning.

The result of unsupervised learning might be less accurate. And why it be less accurate? Because the data is not labeled and algorithms do not know in advance that what the output is going to be which is known in supervised machine learning.

Furthermore, the information obtained by the algorithm may not always correspond to the required output. So that is also we need to clean that particular data. Therefore data cleaning is a big step required here and the user has to understand the map of the output obtained with the corresponding label. So the user has to understand that how can the mapping be done whereas the machine used to do that in supervised learning. Therefore these are the few advantages and disadvantages of unsupervised learning.