Understanding Supervised and Unsupervised (Machine) Learning

As you start with your Machine Learning, you will get to hear a lot about the terms Supervised and Unsupervised learning. You will find a lot of blogs, videos etc… explaining and differentiating these 2 types. This is just another attempt to explain with some examples.

Supervised learning

Supervised learning means you (a supervisor) is training the machine to identify few patterns from the data you provided. Here the data will have some clear indications (labels) about the pattern for the machine to learn from. Machine can use this learning to find similar patterns in the new dataset.

Taking an example, you are giving few Apples and Oranges to a kid and you are identifying some of them as Apples and others as Oranges based on their characteristics (colour, hardness etc…). Next time you give him an Orange, he will be able to identify it as Orange from his previous experience.

Classification and Regression are 2 models of Machine Learning falling under the Supervised Learning category. We will learn these in detail in later posts, but just to give you an overview..

Classification model is used for classifying the input data (classifying emails to spam or not, medical diagnosis – cancer or not etc…)

Regression model is used to identify a continuous relation of the output values with given input values (predicting the rent amount for houses with the listed different features)

Unsupervised learning

In this type of learning, the data provided is not labelled or classified. Machine tries to find some patterns or similarities in supplied data and group or sort it.

considering a similar example which we discussed for supervised learning, you are giving a basket of Apples and Oranges to your kid. You are not giving any special instructions about the fruits in the basket, but your kid will still be able to form 2 groups, 1 of Apples and the other of Oranges. He/she is able to do it by observing the characteristics like colour, softness etc…

Clustering and Association are 2 common models falling in this type of learning.

Clustering is forming groups from the given input based on similarities (example below)

Association is used mostly in identifying a customer’s buying pattern, which in some cases called as Market basket analysis. How many customers buying milk are buying bread (for example)

Hope you enjoyed reading this post. Please feel free to share your thoughts in the comments section.