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Machine Learning is broadly classified into three types namely Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Reinforcement learning is still new and under rapid development so let’s just ignore that in this article and deep dive into Supervised and Unsupervised Learning.
Before moving into the actual definitions and usages of these two types of learning, let us first get familiar with Machine Learning. Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed, this is just the textbook definition of Machine Learning as this article is mainly written for the newbies of Data Science and Artificial Intelligence field let me make this more clear and interesting for you so that you can understand and interpret it better.
Let us consider a baby as our machine and we need to help the baby learn the different numbers in our number system. In order to help the baby learn we need to show the baby a different number and tell what each number is.
Doing this part repeatedly helps the baby learn and memorize the numbers. This is nothing but the ability to automatically learn and improve from experience without being explicitly programmed i.e. Machine Learning.
Let us start again with the classic textbook definition of Supervised Learning and make ourselves familiar with the baby example that we earlier took. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
I hate the definitions that are written in any textbook as they are so formal to understand, rather I would prefer a friend explaining the definition in his own thoughts. In the long run whenever I try to recollect a definition, eventually the explanation given by a friend with an example pops up and makes my life easier. So, in this article let me be that friend to you.
Let us again take the baby example we considered earlier, in this case, we need to make the baby learn and identify the different fruits that we have. Let us consider Apple and Orange as our two fruits and we start with showing these two pictures to the baby. We also tell the baby which picture is which fruit.
Looking at those pictures the baby learns that fruit will be round and red color fruit is Apple and orange color fruit is Orange. Now let us show the baby a new picture of Orange and ask him to find whether the picture is Apple or Orange.
The baby predicts that the fruit is Orange. The baby correctly predicts the fruit as Orange because we have labeled the two fruits like Apple and Orange into two categories and has asked the baby to learn what fruit is what. This is how Supervised Machine Learning works if we replace a machine with a baby.
Supervised Machine Learning is further classified into two types of problems known as Classification and Regression.
From the name itself, we can get to know that this is a Machine Learning problem where we need to classify the given data in two or more classes. The above example that we have taken is a Classification problem as we need to classify the given pictures into either an Apple or Orange class.
When we have only two classes to classify our data then it is called Binary Classification. But in real-world data, we tend to have more than one class and it is called Multi-Class Classification. These types of learning are used by the majority to identify the spam emails, classify the customers, to check whether a customer Churns from the operator, and many more use cases.
Also Read: Career in Machine Learning
Regression on the other hand deals with continuous data such as predicting your salary based on the experience. In this case, we do not need to put the data into any classes but need to predict the continuous value based on the continuous data we have.
These types of problems have continuous columns in their data set whereas Classification tends to have categorical columns. These types of learning are used to predict the financial growth in the next quarter for any company, student marks based on his previous marks, and many more.
Unsupervised learning is a type of machine learning that looks for previously undetected patterns in a data set with no pre-existing labels and with a minimum of human supervision.
In contrast to supervised learning that usually makes use of human-labeled data, unsupervised learning, also known as self-organization, allows for the modeling of probability densities over inputs. Let us consider the baby example to understand the Unsupervised Machine Learning better.
Let us use a group of cats and dogs’ pictures as input in this example, in earlier examples the baby knows that the pictures are of Apple and Orange as we have labeled and categorized them.
In this case, the baby doesn’t know anything and hence cannot categorize which one is a cat and which one is a dog. But can tell that few of the pictures look similar when compared to the other few. In this case, we cannot label the data, but we can still find patterns in the data. This is how the Unsupervised Machine Learning works.
The above taken example clearly describes the Clustering problem, we need to cluster our dataset based on the patterns that we find in our data. Clustering is a very important Machine Learning problem and many companies tend to use this technique to find valuable patterns, insights from their data.
In this article, we got to know about the different types of Machine Learning, got to understand those taking an easy to understand example, investigated the further divisions of each learning. This article covers only the basics of the Machine Learning problems, each type of problem has different types of Machine Learning Algorithms.
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