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Real-time Applications of Machine Learning

Machine Learning

Machine Learning is an advanced technology branching out from Artificial Intelligence a couple of years ago. With rapid growth, Machine Learning is applied across various industries. Over the years, ML has created several breakthroughs in the healthcare and manufacturing sectors. Additionally, automation has allowed various enterprises to speed up their business processes and enhance productivity of employees.

Currently, we can see various instances where Machine Learning has helped us and made it easier to lead a simple life. Individuals, as well as, organizations have reaped a wide range of benefits since the evolution of ML. The days where manual effort mattered have long gone now. Implementing automation is the new trend to eliminate time-consuming tasks that are non-critical for business operations. In addition, with the increase in the collection and analysis of data, ML algorithms are utilized to extract vital information.

With the advancements, even ML algorithms constantly evolve. The automated functions are majorly being applied to medical diagnostics, research purposes, web applications, electronic devices, cybersecurity, and so on. Let us take a look at some of the real-time applications of Machine Learning.

Top Applications of Machine Learning

a. Commute

Individuals today can know how much time it would take to reach their destination, and prediction of traffic en route. Various navigating apps have started applying Machine Learning to their programs currently. Among these apps, riding apps like Uber utilize ML in estimating the price for assisting individuals in reaching their destination. In addition, Uber also specifies the availability of rides present in the location of clients through optimized ML algorithms. This is one of the real-life instances where individuals utilize ML for commuting.

b. Communication

Organizations use the most formal way of communication internally as well as externally. Electronic mails or E-mails are the most commonly used communication mode in this case. However, employees or clients can be overwhelmed with hundreds of emails from numerous sources. Google applies Machine Learning to classify emails based on promotional emails, social emails, primary emails, and so on. Moreover, ML algorithms are even designed to filter spam and junk mail from the inbox.

c. Detection of frauds

Organizations in finance use ML algorithms to detect fraud. With abundant data on daily transactions, financial institutions apply ML to identify fraudulent ones. Besides, fraud detection, organizations apply ML in determining whether to accept clients for credit purposes. ML is used in such cases to assess clients and make informed credit decisions whilst analyzing risks. Moreover, various software has been developed using ML algorithms that assist banking employees in carrying out their daily tasks effortlessly.

d. Diagnosis

Machine Learning uses a wide range of tools and techniques in the medical sector. It assists healthcare organizations to assess medical data and interpret the results extracted to offer useful information. In addition, ML is used in equipment that supports monitoring patients. Over the years, various instances have showcased that ML has supported in estimating major disease breakthroughs, therapy, research purpose, and so on. Besides, ML is integrated with AI for efficiently managing patients in medical institutions.

e. Predictive Analytics

ML algorithms use data groups present within organizations for analyzing and predicting patterns. The algorithms are designed to help analysts in setting the conditions required for predicting the trends. Applying ML in predictive analytics entitles organizations to improve their products and stay competitive in the industry. In addition, businesses applying ML can easily calculate the probability of defects that may occur due to uncertainties.

f. Image Recognition

The major application of Machine Learning can be seen in image recognition. ML algorithms used in this application enable users to identify an image based on the pixels. Color or black and white images can be easily intensified with machine learning to identify objects. For instance, healthcare businesses use ML to identify abnormalities in X-rays. Moreover, even facial recognition software utilizes machine learning to assess the commonalities present within thousands of images.

g. Identification of Explosives

Detecting explosives is a high-risk job that individuals have to carry out. Without proper equipment, life can be lost due to high-impact explosives. Machine learning is instilled with the radar equipment used by such personnel to assist in detecting explosives. The radar systems and other such equipment used in detecting explosives utilize the algorithms to transform data and multi-sensor fusion. This allows individuals to make decisions quickly and act as required based on the threat level.

To Sum Up:

These applications make it evident that Machine Learning has supported organizations in enhancing their productivity and operations. Irrespective of its size and location, any organization, enterprise, or small business can utilize machine learning to transform its operations. Moreover, businesses that lack experts to implement this technology can secure Machine Learning Consulting Services. Highly proficient experts in this domain assist organizations in instilling ML as per business requirements. This entitles organizations to seamlessly introduce Machine Learning into their internal structure, thereby profiting from this advanced technology.