2 Classification and Regression Using Supervised Learning
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Supervised versus unsupervised learning
One of the most
common ways to impart artificial
intelligence into a machine is through machine learning.
The world of machine learning is broadly
divided into “Supervised” and “Unsupervised learning”.
There are other divisions too, but we'll
discuss those later.
Supervised learning
It is the process
of building a machine learning model that is based on labeled training data.
For example, let's
say that we want to build a system to automatically predict the income of a
person, based on various parameters such as age, education, location, and so
on. To do this, we need to create a database of people with all the necessary
details and label it. By doing this, we are telling our algorithm what parameters
correspond to what income. Based on this mapping, the algorithm will learn how
to calculate the income of a person using the parameters provided to it.
Unsupervised learning
It is the process
of building a machine learning model without
relying on labeled training data.
In some sense, it is the opposite of what we
just discussed in the previous paragraph. Since there are no labels available,
you need to extract insights based on just the data given to you.
For example, let's
say that we want to build a system where we have to separate a set of data
points into multiple groups. The tricky thing here is that we don't know
exactly what the criteria of separation should be. Hence, an unsupervised learning algorithm needs to separate the given
dataset into a number of groups in the best way possible.
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