Unsupervised Machine Learning is the second category of Machine Learning.
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Unsupervised learning
What is Unsupervised Learning?
Mathematically unsupervised learning is where you only have input data X and no corresponding output variable. The goal for unsupervised learning is to model the underlining structure or distribution in the data, in order to learn more about the data.
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| Eg- unsupervised machine learning process |
In simple terms, An Unsupervised Machine learning approach the data instances of a training. data set do not have an expected output associated to them instead unsupervised learning algorithm detects pattern based on initial characteristics of the input data.
An example of machine learning tasks that applies unsupervised learning is,
Clustering. In this task similar data instances are grouped together in order to identify clusters of data. In Given Image, you can see that initially we have different varieties of fruits as input.
Now these set of fruits as input X are given to the model. now once the model is trained using unsupervised learning algorithm, the model will create clusters on the basis of its training. it will group the similar fruits and make their cluster.
Let's take another example of it.
In this image below shows an example of unsupervised learning process. this algorithm processes an unlabeled training data set, and based on the characteristics.
It groups the picture into three different clusters of data, despite the ability of grouping similar data into clusters.
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| Eg of unsupervised machine learning |
The algorithm is not capable to add labels to the crop. The algorithm only knows which data instances are similar, but it cannot identify the meaning of this group.
So now you might be wondering why this category of machine learning is named as unsupervised learning. so these are called as unsupervised learning because unlike, supervised learning ever there are no correct answers and there is no teacher algorithms are left on their own to discover and present the interesting structure in the data.
Some of the popular unsupervised learning algorithm.
so we have here k-means, Apriori algorithm and hierarchical clustering again.
Examples of Unsupervised Learning-
1.Suppose a friend invites you to his party and where you meet totally strangers. now you will classify them using unsupervised learning, as you don't have any prior knowledge about them and this classification can be done on the basis of gender, age, group, dressing education qualification or whatever way you might like.
Now why this learning is different from supervised learning. Since you didn't use any past or prior knowledge about the people. you kept on classifying them on the book as they kept on coming. You kept on classifying them. this category of people belong to this group. this category of people belong to that group and so on.
2. Let's have next example Suppose you have never seen a football match before and by chance you watch a video on the internet. now you can easily classify the players on the basis of different criterion, like player wearing the same kind of jersey or in one class player wearing different kind of jersey.
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| Football match |
Are in different class or you can classify them on the basis of their plane style, like the guys are attacker, so he is in one class. he's a defender he's in another class or you can classify them whatever way you observe.
The things so this was also an example of unsupervised Learning.
Let's move on and see how Unsupervised Learning is used in the sectors of banking health care, and Retail.
In Banking sector-
In banking sector it is used to segment customers by behavioral characteristic, by surveying prospects and customers. To develop multiple segments using clustering.
In Healthcare Sector-
In healthcare sector. It is used to categorize the MRI data by normal or abnormal.
It uses deep learning techniques to build a model, that learns from different features of images to recognize a different pattern.
In Retail sector-
In retail sector it is used to recommend the products to customer based on their past purchases. it does this by building a collaborative filtering model, based on the past purchases by them. Let's discuss the third and the last type of machine learning. that is enforcement learning .
Next is reinforcement learning..?
Click the question for next....-
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