What are some real-world applications of Machine Learning?
No one can deny the fact that our current lives revolve around the internet. Almost a decade ago, we were reliant heavily on manual methods, but now technology is our best friend, especially machine learning applications.
Who would have thought that technology will be so advanced that we could track the exact status of traffic on the roads? Or, that food will be just a click away. Or, did any of us ever think about saying ‘ok Google’ or ‘Hey Siri’ to command personal digital assistants.
Basically, it is science that has made technology so profound. If we take a sneak peek, the applications we are using daily are Artificial Intelligence and Machine Learning in disguise. Let us have a look at some real-world applications of Machine Learning.
Uses of Machine Learning applications in the real world:
|Traffic alerts||Image Recognition|
|Video Surveillance||Sentiment Analysis|
|Product Recommendation||Online support using Chatbots|
|Google Translate||Online Video Streaming Applications|
|Virtual Professional Assistants||Machine Learning Usage in Social Media|
|Stock Market Signals Using Machine Learning||Auto-Driven Cars|
|Real-Time Dynamic Pricing|
The reason Google Maps know the fastest route you are on is the combination of historical data of that route and real-time techniques. While using Google Maps, the app has access to your location, average travelling speed, answers to your traffic-related queries, day, and time. All such data is stored and captured by the application. AI and machine learning algorithms use the data to make the right conclusions as required by individuals.
Video surveillance is one of the most advanced machine learning and AI applications. To fetch valuable information from automated surveillance devices, a video gives a better opportunity than any other service. As compare to human minds and manual surveillance, machines make it easy to outlook objects.
Different purposes of Video surveillance are listed below:
Because of their accuracy, surveillance footages are the best machine learning dataset, but it must be noted that at times footage can be hard to obtain. The objects can easily recognise the targets from standard images with the help of the object detector.
Travelling can be quite thrilling at times, but the puzzle here is to understand the native language of that place. To solve this common problem, Google has launched an application for easy translation of any language.
The ‘Google Neural Machine Translation’ used by Google can take in numerous languages and dictionaries and transform any desired sentence into the desired language.
Virtual Professional Assistants:
Nowadays, machines are becoming smarter in adapting human-like behaviours. Commanding Google assistant or Siri to wake you up early at 6 AM or to navigate you to your favourite theatre, we live in a world that nobody ever thought of! Booking an Uber, ordering food from Zomato, all runs based on machine learning algorithms.
Imagining and recognising an image is an effortless task for humans. But if we talk about computer systems, images are just some numerical values. This is why image processing algorithms are used to look for patterns in different forms of images, i.e. videos, still images, or graphics. Computer systems work on pattern reorganisation algorithms of machine learning to recognise any visual.
If you can unlock your phone by merely looking at it, this is also machine learning. Eighty nodal points on a human face are recognised by the phone camera and machine learning technologies while unlocking a phone using a person’s face. Face recognition phone unlocking is one of the typical machine learning applications used nowadays.
Other uses of image recognition are:
Online support using Chatbots:
Nowadays, almost every app has a ‘chat with us’ section. These chatbot sections are running concepts of machine learning. Chatbots are basically used to get quick answers for the respective queries raised.
To give precise answers, the ‘decision tree’ technique of machine learning is used by these chatbots. Decision trees help the machines learn to answer queries quickly to help the customers in need.
Machine Learning Usage in Social Media:
Machine learning helps us in our day-to-day activities at different social media applications. Based on the data collected from social media, AI, and machine learning give detailed insights. Marketers get access to the following by using machine learning algorithms:
New technological changes are being openly accepted with the progress of the digital world, career opportunities for Artificial Intelligence and Machine Learning are skyrocketing. To start their journey, and help individuals achieve their dreams through education, SRM in collaboration with LEARNXT, offers various degree and certificate courses in AI and machine learning. With the latest applied data science curriculum delivered by world-class faculty, you can have lifetime access to the courseware on the AI-based StudyNxt platform.
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Students will be given real-world business problems relevant to the concepts covered in the course. These are prototypes of typically analytics-driven problems or projects. They also have to apply the conceptual knowledge learned through the classroom lectures and the appropriate analytic techniques to meet the stated objectives and solve the problem.
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