Machine Learning is undeniably one of the most powerful and influential technologies of the 21st Century. One of the buzz-words in modern times, the capabilities of ML is often compared to that of humans. In short, Machine Learning is defined as the ability of machines to think and act like humans in certain situations. As such, a certain question that always arises in the mind of the commoners is that, ‘where all Machine Learning can be used’? There is no definite answer to this question. Typically Machine Learning models have been used in the prediction problems, in classification problems, image recognition, voice and speech recognization, autonomous vehicles, developing agents to play video games and stuff. In this article, we will be discussing the major application of Machine Learning. So, let us get started.

Classification Problem

Suppose, you are a manager at one of the banks. You are in charge of sanctioning loans to customers. However, if any of the customers are unable to pay back, you will be held responsible and accountable for this fault. So, you have decided to use ML models to classify which of the loans will eventually convert to bad loans. You have the data of customers like their income, their past employment history, their repayment history, the amount they have taken as loan and their credit score. By applying certain ML algorithms, you will be able to predict, with a good accuracy, which customers will eventually pay the loan. This type of problem is called a classification problem. ML algorithms are usually used in this type of problem.

Regression Problem

Suppose you want to purchase a house in the next 5 years from now and want to predict like what will be the price of the house in the next few years. You have the availability of data like the present price of the house, the number of balconies, the number of floors, the type of neighborhood and stuff. Since, we will be predicting the price, which is a type of continuous value, this type of problem falls under the category of Regression. ML models can very easily solve these types of problems.

Computer Vision

Humans can easily distinguish between 2 different things- say cats and dogs. However, this is not an easy task for a machine. Similarly, we can easily recognize and identify our friends and relatives. The same goes for the identification and recognization of traffic signs and signals. The easier is for humans, the more difficult is for machines because they see every image as a combination of pixels. However, on training them on a certain type of dataset, they can produce very accurate results. The visual ability of humans as perceived by machines is called Computer Vision. In this technologically advanced world, we see surveillance systems developed by some countries. These work on the concept of Facial Recognization, a sub-branch of Computer Vision. This way ML models are transforming the world of Computer Vision.

Reinforcement Learning

Many of us like playing games. Have you ever imagined training an ML model to play games for you? This is now becoming a reality with the introduction of Reinforcement Learning. People in the field of Reinforcement Learning have developed agents to play games like Super Mario, Chess, Angry Birds, etc. The whole concept of Reinforcement Learning is based on the concept of rewards and punishment. An agent chooses a suitable step in the game by analyzing the reward and punishment. Autonomous Vehicle also falls under the category of Reinforcement Learning.

This blog on Medium lists quite a few innovative uses of ML. This will spark quite a bit of interest in you, which can be further enhanced by reading this brilliant write-up by Forbes. We know it is quite interesting to know such tremendous applications of Machine Learning. Probably you will need more such information and facts from reliable sources. Another great write-up published by Forbes can be quite an interesting read in this context.

We have discussed quite a few numbers of applications of Machine Learning and we are sure we have invoked a spark of interest in you to find out more about these fields. Happy Researching.