The Rise of Machine Learning & its Implementation in Commercial Sectors

Machine Learning

We live in a society where technological advancements are coming in faster than ever. The level of development we used to see in a decade is coming up in a year, and a year’s worth of innovation is happening in a matter of days. 

Although several factors are at the heart of this unprecedented growth of technology, one has created a larger space for itself. And that’s none other than machine learning.

While the term machine learning certainly makes one think about the horrors of robots taking over the world, it is considered far-fetched by technology experts

This blog will share how machine learning improves our lives, along with some applications in our modern-day world. 

What is Machine Learning?

Machine Learning is an emerging technology that lets computers leverage statistical models and complex algorithms to study different concepts to learn and adapt. It’s a phenomenon where machines comprehend patterns and repetitive information derivation independently. 

There are so many things that machine learning has helped develop, and we will be sharing examples of those as well. Almost every other app and game we interact with these days uses machine learning at heart. Only their application level is different, and their purpose is to give us a more personalized experience. 

Implications of Machine Learning:

Here are the top areas where machine learning has had a transformative impact.

Real Estate:

Real estate is one sector that seems to benefit the most from machine learning. There are thousands of real estate websites providing online property listing services, where users can browse through thousands of properties on the go.

Those websites use machine learning algorithms that try to guess your preferences and apply the information to suggest listings according to your preferences. It sure takes time, but once you have browsed through different properties, the recommendation game gets stronger over time.

Telemedicine & Healthcare:

There is an endless list of ways the healthcare sector uses machine learning. The most prominent domain seeing advances is healthcare application development. It is improving the inclusive standards of healthcare and making systems more efficient. 

These healthcare apps are truly remarkable, whether it is OCR to read and understand physicians’ handwriting for generating digital reports or using historical data to find iterations to identify potentially harmful disease variants.

Telemedicine apps developed by a healthcare app development company also leverage machine learning. Prescription and symptom-checking apps use machine learning to provide virtual healthcare services to patients who do not have access to qualified doctors in remote areas. Pharmaceutical companies use ML for drug research, discovery, and product placement. Wearables like Embrace are using ML to save the lives of epilepsy patients via emergency care. 

Banking and Finance:

Machine learning applications in banking and finance are just as big as the industry itself. They use it for highly critical functions these days. Examples include fraud detection, automatic stock and forex trading, and financial advisory.

Each of these applications needs access to extensive data to learn patterns related to fraudulent activity, a successful trade, or an investment opportunity. The systems can analyze tons of data within seconds and provide answers that ensure the best possible outcomes.


Forensics is benefitting greatly from applications of machine learning. Access to the analysis of millions of case files and police records allows systems to match new information with historical data patterns. It creates possible clues for solving cases. 

It also risk-proofs law enforcement agencies by providing safer alternatives in dangerous situations, saves time during investigations, and helps the authorities reach conclusions. 

Waste Management:

The application of machine learning is also improving waste management systems. Waste management companies have utilized ML functions from multiple angles. Systems are being designed for efficient waste collection from domestic and commercial areas.

ML also helps save energy expenses and allows investment into better systems while optimizing routes. Waste identification and reuse opportunities are also emerging trends in ML.

Ecommerce & Retail:

We have all had that moment when we typed something on Google and ended up seeing related ads on Instagram or Facebook feed the very same day. The uncanny aspect is how retailers use machine learning applications to reach more relevant customers. Companies use big data from vendors to show relevant ads relevant to your searches and needs. 


We live many practical examples of machine learning in the automotive industry without knowing them. Lane-change assistant, automatic braking and parking, and smart energy management for extended mileage are some popular implications of ML in the automotive industry.

Practically, a system learns about the variables of a particular situation and iterates them countless times to determine the best result for each scenario. 

Tesla’s driverless cars are also exemplary applications of machine learning in the automotive. These cars take you from Point A to B without a human driver.  


Let’s simply say that modern space travel would not be imaginable without the diverse applications of machine learning. We want to learn more about how difficult life is on the other planet, but we can’t risk sending earthlings.

Thus, we send machines with machine learning algorithms on board. They regulate themselves and undertake challenging tasks through constant data analysis and automated learning. 

Some examples of ML applications in space are:

  1. study of black holes 
  2. identifying and exploring new galaxies
  3. sending information through satellites

Entertainment Sector:

Content creation is the best application of ML in the entertainment sector. So many projects require the creation of vast realms, but it is simply impossible to bring them to life using standard 3D modeling.

It would take a lot of time and resources to complete manually. Thankfully, systems can now use machine learning to understand different elements of an environment and create logical iterations. 


It is evident that machine learning is the future of pretty much every industry. If you want to be a part of its bright future, you need to comprehend and escalate the extent of machine learning applications. And more importantly, integrate machine learning models in our business to automate functions and increase efficiency. 

You can hire nyc, Miami developers or any other major part where machine learning is slowly but progressively going mainstream and automate your business to achieve exponential growth.