TABLE OF CONTENT
List of ML Applications
(1) Medical Diagnosis
(2) Online Fraud Detection
(3) Stock Market Trading
(4) Language Translation
(5) Sentimental Analysis
(6) ML in Security
(7) Product Recommendations
(8) ML in Automobile Sector
(9) Speech Recognition
(10) ML in Banking Sector
(11) GPS Tracking
Machine Learning Applications - UPDATED 2021
Medical Diagnosis with ML
The next application of Machine Learning is to assist physicians in diagnosing medical problems through medical algorithms. Unfortunately, many of these algorithms do not perform as well as doctors’ intuition.
Generally, a machine can diagnose about 60% of all conditions, by applying algorithms to medical diagnostic data. However, as a diagnostic specialist, you will probably not be content with the machine diagnostic results. For the specialist, the proper diagnosis always takes time.
Improvement in the diagnostic accuracy can be substantial & doctors are extremely frustrated with ML system.
Although the machine is indeed very efficient, it is not yet up to the task. It often fails to recognize symptoms that the expert can easily recognize. But in the mean time ML experts are continuously applying their efforts to upgrade the level of technology to the next level.
Online Fraud Detection
Machine learning applications make the system efficient to detect frauds much more accurately, and have robust, and multi-data-set analytical platforms.
The systems can not only understand a credit card fraud but also explain what the fraud pattern is, and are at the point where they can actually tell the customer where the fraud is happening, or that there’s some sort of suspicious activity taking place.
move money back to the customer and manage fraud that is taking place, which is another way to automate fraud detection. Adoption of new applications and infrastructure, we believe, will increase as automation happens in this sector and continues to grow.
Stock Market Trading
In market there are so many trading apps which provides a best experience to the users. These apps are the best example of machine learning application on stock market trading.
The real-time ups and downs on the graph highlights the neural network principle, which is one of the widely used techniques of machine leaning.
This technology makes the system highly profitable with real time trading and investing. It removes a number of barriers or obstacles, what a person experience during traditional way of trading or investing.
Language Translation with ML
Language translation using machine learning was based on neural networks. In a neural network, we train a hierarchy of algorithms called nodes to do a particular task, and they learn to associate inputs (in this case phrases) with outputs (in this case, translations).
through the use of machine learning a language translator is produced that provides simple multi-language interpretation at fast speed, enabling effective voice recognition systems.
In these applications the machine learning approach to voice processing is a win-win. The customer benefits from
fast processing with their desired language, and the system benefits from having the data already built in, thus no need to start with a broad training and classification effort. In essence, the machine learning approach is easy and elegant.
Sentimental Analysis using ML
Sentimental analysis done through machine learning can take the same emotional view that humans do, as demonstrated here. And as mentioned, machine learning can often make much better decisions than any human, given the right tools.
Most of the Social Media sites are better shows the example of sentiments. The heart rate on facebook determine whether an individual is feeling contented, when he sends his friends some heart-touching post or more content-less when he posts things like status updates with jokes and funny jokes of famous actors.
ML in Security
A few machines are being used in terms of security systems. For instance, machine learning is being used for security solutions such as automated breach detection systems and data tracking.
A few factors can make the machine prone to hacking. One of the factors is its perception. The machine can be trained to perceive with low input such as images and objects and act accordingly.
Similarly, a machine can also be trained to detect certain security loopholes in an application. All it needs is to get enough data, for instance, security breaches.
The data can be fed through machine learning techniques, so that it can act accordingly to the security breaches. Through this analysis of data a system can monitor each and every movement or activity in any individual.
The applications of machine learning on product recommendation and user interaction are becoming more relevant. Customers expect to interact with websites and applications in real-time.
This improves customer experience, increases engagement, and gives the platform a competitive edge over the competition. Now a days these kind of technology adopted by big brands like Amazon, Netflix, wallmart, ebay and many more.
The product recommendation techniques using machine learning is profitable to the end user (retailers, consumers),
generating the desired business outcomes. The product recommendations are personalized and focused on the user, such that the end user can have a better experience. This automated technology making the system profitable through its implementation.
ML in Automobile Sector
The rapid growth of the automobile sector will have an effect on the growth of the machine learning market. The sale of vehicles is also increasing.
According to data from the International Organization of Motor Vehicle Manufacturers, there were 1.48 billion vehicles on the road worldwide in 2015 and that is projected to grow to 2.3 billion by 2025.
Also, the adoption of machine learning in the automotive sector will lead to innovation in the automobile industry. In order to increase the sales of the automobiles, the companies are adopting ML technology to gain more understanding of consumer preferences and thereby, increasing the marketing budgets in an automated manner.
Despite the skepticism, there is definitely a place for machine learning. For voice recognition applications, in particular, it seems as if machine learning will be the way to go.
Over the next several years we will see hundreds of voice recognition applications. Some are natural-language applications that will do tasks such as responding to user queries or providing other voice-activated options.
Some are social applications and will respond to social cues and some will be applications with machine learning functionality built in.
The applications will be very different; voice recognition will never be confined to simple text processing, which is the standard for speech recognition. You have used Alexa or the Google Assistant or Siri on your mobile phone, or Facebook Messenger on your laptop are the best example of machine learning applications.
ML in Banking
The best use of Machine learning application in Banking sector will require the high efficient ML application to be built and deployed in the Financial Services domain to enhance the bank’s digital banking services.
The Bank’s applications can be built to run on the banks’ mainframe and create, improve and improve a software application over time with continuous learning.
A good number of banks are linking their handy task with machine learning. A few of the task has already been assigned to ML but a few are still on research.
GPS has been witnessing a rapid growth in applications. Some applications of machine learning is used for GPS tracking, traffic monitoring and weather forecasting.
Though GPS hasn’t replaced human manual navigators yet but GPS tracking has become a reliable tool for machine learning. Machine learning will be incorporated to determine location of autonomous vehicles and track traffic flows and enhance road safety.
If GPS can learn about location of vehicles, then one can predict traffic flow and perform all sorts of applications. GPS is an exciting field with a lot of work being done in the
field of machine learning. Machine learning will add a greater value for machine tracking and predicting future events. A smart GPS track device can achieve things in planning in applications such as traffic monitoring, location tracking, environment mapping and much more.
Best 11 Machine Learning Applications in 2021
Application of machine learning is trending now a days, it makes both the firms and the industries to adopt faster technology. With the application of machine learning, it is very easy to predict how a machine will react, to measure and adjust its actions. It is the future of technology and no body can deny it.
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