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# Unsupervised Learning | Machine Learning Beginners

In our previous article, we understand basics of supervised learning.

In this article, we are going to know what unsupervised learning is. And try to understand this by using various example.

The Unsupervised Learning is learning by observation.

## Examples

As we observe the things surrounded by us and then we try to categorized the thing into different different category. Assume that we are standing in the kitchen and observe the things in the kitchen.

We observe things and try to categorized them into different groups. Like first group is of electronic devices like blender, fridge, toaster etc. Another group is of jars that contains different different things inside it. Another group is of mess like dinner set, bowls, plats etc.

For example we can take the employees of the company and then partition them into department wise.

Another example is that take student of the class, and group them into two parts. One part is that who we wear glasses and another part is who doesn’t wear the glass.

In simple words we can say that unsupervised learning is learn by observation which means that they observe the thing and after that it categorized the things into different different groups.

Each element inside the same group has same behavior but each different group has different types of behavior than other group.

For example we can take class of student and partition into the two parts. one part is that the students are passing the exam and another is that the student fail in the exam.

So group of passing student contains all the students who are passed the exam and another part contains students who are failed in exam. So this both groups have different behavior than one another but inside the same group the members have the same types of behavior.

Unsupervised Learning is works same manner as mentioned above.

Machine takes the bunch of data, and the goal of unsupervised learning is that discover the group of similar behavior or characteristics that is called Clustering or to determine how the data is distributed in the space known as Density Estimation.

## Categories Of Supervised Learning

• Clustering
• Exclusive (Partitioning)
• Agglomerative
• Overlapping
• Probabilistic