There is no doubt that machine learning is one of the best modern innovations.
It played a fundamental role in enhancing different industrial and professional processes, as well as helping in our day to day life. And there wouldn’t be any wrong to say that, there are so many advantages of machine learning.
And to understand its advantages, I am just going to look at some of the top real-life machine learning examples.
So here we go:
Image recognition is one of the most popular innovations when it comes to machine learning. Image recognition technology is now being used widely. With the help of image recognition technology, you can identify an object as a digital image. It happens using an intensity of black and colors.
In the real-world, image recognition is now actively used to label an x-ray as cancerous or not, assign a name to a photographed face or tagging on social media sites, recognize handwriting, and so on.
Machine learning is also being used for face recognition in videos and photographs by local authorities. The system can quickly identify commonalities and match faces pulling up profiles utilizing a database of people they already have.
Speech recognition is also one of the widely used technologies in today’s time. Speech recognition nowadays exists in our smartphones and other smart devices, as machine learning can translate speech into text. Also, certain applications can easily convert live voice and recorded speech into a text file.
Talking about some real-life examples, we are all well aware of voice search, voice dialing, and appliance control. Moreover, speech recognition is one of the tools used in education.
Voice assistants like Google Assistant, Amazon Alexa, or Siri by Apple are all using machine learning to communicate, pull up answers to your questions, or helping you to search details online.
Machine learning is also being actively used for diagnosing diseases. Many physicians use chatbots with speech recognition capabilities to discern patterns in symptoms.
Machine learning is being used to form a diagnosis or recommend treatment for the disease. Also, oncology and pathology use machine learning to recognize cancerous tissue. Plus, we can use it for analyzing bodily fluids.
Even in rare diseases, facial recognition software and machine learning’s collective use helps scan patient photos and identify phenotypes that correlate with rare genetic disorders. Such technology is useful for screening newborns in a hospital, making it easy for parents to prepare for treatment and issues.
As you probably already know, Arbitrage is an automated trading strategy used to manage a large volume of securities. The strategy is mainly used for analyzing a set of securities using economic variables.
One can use machine learning for algorithmic trading that helps in analyzing a market microstructure. It can also assist in analyzing large data sets and identify real-time arbitrage opportunities. Overall, machine learning helps in optimizing the arbitrage strategy to enhance results.
Machine learning is also being used for classifying available data, which rules set by analysts then define. When the process of classification gets completed, the analysts can calculate the probability of a fault.
Some real-world examples of predictive analytics predict whether the transaction is fraudulent or legitimate, and improve prediction systems to calculate the possibility of fault.
You should also know that predictive analytics is one of the most promising examples of machine learning.
Lastly, one can use machine learning for extracting structured information from unstructured data. Organizations collect vast amounts of data from their customers, and machine learning can be used to automate the process of annotating datasets for predictive analytics tools.
One can use machine learning to generate a model to predict vocal cord disorders, develop methods to prevent, diagnose and treat the diseases. As well as help physicians diagnose and treat problems quickly.
Among other trends is machine learning in education. Here the technology covers different aspects, like voice and image recognition, custom course building, support and many others. The Cognii AI is the best example of the ML usage in edtech.
So those were some of the real-life machine learning examples. I hope this helped you to understand how machine learning is being used actively in
different industries. It influences our lives and makes them more comfortable and secure. Machine learning is not only used by individuals at their homes, and it entered various businesses and local authorities. Maybe you know other sectors where machine learning development company can solve industry challenges. In case if there is anything else you would want to ask, then do feel free to drop a comment below.