top of page


Data scientists use many different kinds of machine learning algorithms to discover patterns in big data that lead to actionable insights. At a high level, these different algorithms can be classified into two groups based on the way they “learn” about data to make predictions: supervised and unsupervised learning.

Supervised Learning
Unsupervised Learning

Supervised Learning

In supervised machine learning, the algorithm is provided with a set of examples such that each example has a label (e.g., whether an email is spam) and the algorithm attempts to “figure out” how to map these examples to their corresponding labels. This process is similar to human cognitive heuristics, but the primary difference between this and human learning is that machine learning runs on computer hardware and is best understood through the lens of computer science and statistics, whereas human pattern-matching happens in a biological brain (while accomplishing the same goals).

Supervised Learning

Diagrams for (Legal IT Insider)-03.png

The Process


Voice assistants

Siri, Alexa, Cortana or any speech automated system in your mobile phone trains your voice and then starts working based on this training. This is an application of Supervised Learning


Understanding Unsupervised Learning

Mathematically, Unsupervised learning is where you only have input data (X) and no corresponding output variables. The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data

Distinguishing between Supervised and Reinforcement Learning:

The difference between supervised and reinforcement learning is the reward signal that simply tells whether the action (input) taken by the agent is good or bad. It doesn’t tell us anything about what is the best action. In this type of learning, we neither have the training data nor the target variables.

Girl with Tablet

Biometric Authentication: unlocking your mobile or smart device.

Biometric authentication allows machines to recognise your biometric identity – it can be your face, thumb, iris or ear-lobe, etc. Once the machine is trained it can validate your future input and can easily identify you.

What is Reinforcement Learning?

Reinforcement Learning is a type of learning algorithm in which the machine takes decisions on what actions to take, given a certain situation/environment, so as to maximise a reward.

bottom of page