An IT Essential: Innovative Applications of Machine Learning

machine learning

Machine learning has gained momentum in the field of computer science over the past few years. Its applications have already transformed the way many industries function and is continuing to impact more and more industries. To simplify, machine learning is a subset of artificial intelligence that makes computers learn and evolve without being explicitly programmed. It enables them to independently learn based on historical data and patterns to draw predictions for the future. Machine Learning can analyse massive datasets and perform different actions based on the nature of the industry it is applied to.

It is a popular notion that machine learning and artificial intelligence are relevant to tech enthusiasts, large corporations or data-driven companies. However, applications of machine learning impact our day to day lives. Here are some practical real-life applications of Machine Learning that we come across in our daily lives.

Specialization in AI and ML @ UPES

Personalized Recommendations

When you purchase products or add them to your cart on an e-commerce site, it is a common sight to see suggestions for similar products. These suggestions are not random, but a part of machine learning. Most e-commerce sites use machine learning to refine and personalize your shopping experience. Based on the user behaviour on the platform – recent purchases, liked products, products added to the cart, preferred brands, product recommendations are made.

The same principle is used by content streaming platforms such as YouTube, Netflix and Spotify. They use machine learning algorithms which analyses and tracks your activities to give suggestions on the content that you are most likely to engage with. As a result, content discovery becomes easier for users as they get personalized recommendations for the content they may like and keeps them more engaged on the platform.

 Virtual Assistants

Use of virtual personal assistants such as Google Assistant, Alexa and Siri is common. These assistants are conversational and help in finding information. When a voice command activates the search, the virtual assistant searches the web to answer a query. It can also save information that can be recalled with a simple voice message in addition to setting reminders, alerts and sending commands to other connected devices. Machine learning plays a crucial role in the functioning of virtual assistants as it collects information and uses this data to provide a tailored experience in your subsequent interactions with them.

Transportation

For most people, checking Google Maps before travelling is a mandatory part of their routine. It helps to check traffic conditions and plan our route accordingly to reach the destination on time. Google predicts your ETA considering factors and data sets – distance, route, time, day and more. For gauging traffic, it does a congestion analysis by saving locations of all devices using GPS which is then used to map the traffic conditions. Machine learning is deeply embedded in Google Maps and continues to evolve and improve with each update.

Machine learning also forms the basis for cab aggregator apps like Uber and Ola. These apps use machine learning to predict the price and ETA at the time of booking. The cab pooling option uses algorithms to decide which rider to pick and drop first based on the data gathered from maps. Details of each trip are stored to analyse patterns and understand peak hours to activate surge pricing accordingly. Moreover, it helps these apps to meet demand and supply by predicting areas of high demand.

 Social Media

The social media universe is a vast ecosystem that consists of billions of users – which essentially means billions of data sets are available to be used. Every time you come across an advertisement, machine learning algorithms are at play. These algorithms collect data from users – their demographic details, preferences and interests to show ads that are most relevant. Apart from ad targeting, machine learning is also the key to smart programmatic advertising that requires very little manual effort.

 Machine learning has also automated customer service to an extent in the form of chatbots. By using a mix of machine learning and AI algorithms, chatbots mimic conversations and are available to resolve people’s queries 24×7. For businesses, having swift and responsive customer service is a crucial element to increase customer satisfaction and that is where chatbots prove beneficial.

 Healthcare

Machine learning in healthcare is now being increasingly used in predictions and diagnosis of diseases. Basis past medical history and genetic analysis, it can predict susceptibility to diseases, help in early diagnosis as well as recommend personalized treatments to the patients. Since machine learning can process massive amounts of data in a flash, it is used to process CT scans, X-rays and MRIs to identify abnormalities with a higher accuracy rate and speed than manual effort. Machine learning technologies have the potential to be a game-changer in the Healthcare Industry in its endeavour of saving more lives.

Machine learning impacts our daily lives in more ways than we realize. It has already transformed industries and is continuing to do so. The endless possibilities in machine learning makes the field even more fascinating. Applications of machine learning are expanding across industries and with it, the demand for skilled workforce is increasing as well. A career in machine learning and artificial intelligence is a highly viable one at this day and age where technology has penetrated at all walks of our lives.

 UPES is making students future-ready by offering B.Tech in Computer Science with specialization in Artificial Intelligence and Machine Learning. We empower students by providing them with a holistic mix of practical, theoretical and industrial exposure.

Also read: How Artificial Intelligence is Revolutionizing? Key Trends in 2020

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