What Is Machine Learning and Where It Is Used?

Machine Learning is a data analysis method that automates the analytical model building. It comes under a branch of artificial intelligence. Artificial Intelligence is everywhere. Possibility is that you are using it in one way or the other and you don’t even know about it. One of the popular applications of AI is this machine learning, in which computers, software, and devices perform via cognition.

Mainly because of new computing technologies, ML today is not like ML of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data.

While artificial intelligence (AI) is the broad science of mimicking human abilities, ML is a specific subset of AI that trains a machine how to learn. Watch this video to better understand the relationship between AI and ML. You'll see how these two technologies work, with useful examples and a few funny asides.

Why is this ML important?

Resurging interest in ML is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage. All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale. And by building precise models, an organization has a better chance of identifying profitable opportunities – or avoiding unknown risks.

Most industries working with large amounts of data have recognized the value of ML technology.  Herein, are few examples of ML that we use everyday and perhaps have no idea that they are driven by ML.

Social Media Services

From personalizing your news feed to better ads targeting, social media platforms are utilizing ML for their own and user benefits. Here are a few examples that you must be noticing, using, and loving in your social media accounts, without realizing that these wonderful features are nothing but the applications of ML.

Face Recognition: You upload a picture of you with a friend and Facebook instantly recognizes that friend. Facebook checks the poses and projections in the picture, notice the unique features, and then match them with the people in your friend list. The entire process at the backend is complicated and takes care of the precision factor but seems to be a simple application of ML at the front end.

People You May Know: ML works on a simple concept: understanding with experiences. Facebook continuously notices the friends that you connect with, the profiles that you visit very often, your interests, workplace, or a group that you share with someone etc. On the basis of continuous learning, a list of Facebook users is suggested that you can become friends with.

Videos Surveillance

Imagine a single person monitoring multiple video cameras! Certainly, a difficult job to do and boring as well. This is why the idea of training computers to do this job makes sense. The video surveillance system nowadays is powered by AI that makes it possible to detect crime before they happen. They track unusual behavior of people like standing motionless for a long time, stumbling, or napping on benches etc. The system can thus give an alert to human attendants, which can ultimately help to avoid mishaps. And when such activities are reported and counted to be true, they help to improve the surveillance services. This happens with ML doing its job at the backend.


Traffic Predictions

We all have been using GPS navigation services. While we do that, our current locations and velocities are being saved at a central server for managing traffic. This data is then used to build a map of current traffic. While this helps in preventing the traffic and does congestion analysis, the underlying problem is that there are less number of cars that are equipped with GPS. ML in such scenarios helps to estimate the regions where congestion can be found on the basis of daily experiences.


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