Machine learning has become a buzzword in the tech world today, and it is growing rapidly day by day. People use machine learning in their daily life without even noticing, for instance, through Google Maps, Google assistant and Alexa. Artificial intelligence and machine learning are among the most significant technological developments in recent history and apart from their popularity, their daily applications are also on the rise. Many companies are now looking to make the most of this technology to enhance their market offering.
Email Spam and Malware Filtering is one very important application used by companies and their employees on a daily basis. Whenever one receives a new email, it is filtered automatically as either important, normal, or spam. Emails people are interested in are sorted into the important or ‘focused’ mail folder with the important symbol, while the system recognises spam emails and stores them in the spam box. The technology behind this is machine learning and algorithms such as Multi-Layer Perceptron and Decision tree are used for email spam filtering and malware detection.
Online Fraud Detection makes use of machine learning to make online transactions safe and secure by detecting fraudulent activity. Every online transaction involves risks of such fraud, through fake accounts, fake ids, and money being stolen in the middle of a transaction. To detect this, Feed Forward Neural network carries out checks to ensure the transaction is genuine. For each genuine transaction, the output is converted into some hash values, and these values become the input for the next round. For these transactions there will be a specific pattern which changes for the fraudulent transaction and, hence, it is detected.
Machine learning is widely used by various e-commerce and entertainment companies, such as Amazon and Netflix, to recommend products to the user. Whenever one searches for a product on Amazon, targeted advertising will then show adverts of the same product while surfing on the same browser. Google understands each individual’s preferences and interests through various machine learning algorithms and suggests the products accordingly. On the other hand, Netflix uses past data on each user to recommend entertainment series and movies, enhancing user experiences through personalisation.
Some other very interesting real-world uses of this technology include self-driving cars, stock market trading and medical diagnosis. There are very few fields that promise to “disrupt” life as we know it quite like machine learning, but many of the applications of machine learning technology go unseen. Some of these applications would have been almost unthinkable as recently as a decade ago, and yet the pace at which scientists and researchers are advancing is nothing short of amazing.