We are living in a world where technological advancements are coming in at a faster rate every day and week. The level of development we used to see in a decade is now coming up in a year, and a year’s worth of innovation is happening in a matter of days. While there are many factors at the heart of this technological growth, one has created a larger space for itself: machine learning. While the term certainly makes you think about Skynet and the danger of machines taking over the world, the truth is far from it. We are sharing everything you need to know about machine learning applications and how it is improving our lives as we speak.

What is Machine Learning?

Machine Learning is a process that lets computer programs leverage statistical models and complex algorithms to study different concepts. It follows patterns and repetitive derivation of information without anyone asking them to do so. This is something that used to be extremely crude during inception, but now is a sophisticated work in progress.

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 that we are interacting with these days is using machine learning. Only that their application level is different. Their purpose is to give us a more personalized experience. Here are the top areas where machine learning has had a transformative impact.

1.      Real Estate

One of the sectors that seems to be benefitting the most from machine learning is the real estate sector. So many websites are providing online property listing services. Users browse through and find their dream property. They have leveraged ML algorithms that are trying to guess your preferences and utilize the information to suggest properties to meet your requirements. It is a long process but once you have browsed through different properties, the apps always suggest locations that you would end up liking.

2.      Healthcare Sector

There is an endless list of ways in which healthcare is using machine learning applications. It is improving the overall standard of healthcare and making systems more efficient. The biggest domain seeing advances is healthcare application development.

Whether it is OCR to read and understand the handwriting of physicians for generating digital reports or using historical data to find iterations of a genome to identify potentially harmful variants of a virus, the applications are truly endless.

Telemedicine apps developed by app developers in Chicago 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 are using ML for drug research and discovery and product placement. Wearables like Embrace are using ML to save lives of epilepsy patients via emergency care.

3.      Banking and Finance

The applications of machine learning 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, financial advisory. Each of these applications need access to large data to learn patterns that could relate to fraudulent activity, a successful trade, or an investment opportunity. The systems can analyze tons of data within seconds and to provide answers that ensure the best possible outcomes.

4.      Forensics

Forensics is benefitting greatly from applications of machine learning. Access to analysis of millions of case files and police records allows systems to match new information with patterns of historical data. It creates possible clues for solving cases. It also risk-proofs the law enforcement agencies by providing safer alternatives in dangerous situations. This saves time during investigations and helps the authorities reach conclusions. They can then diffuse sensitive situations readily.

5.      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.

6.      Retail and Ecommerce

We have all had that moment when we typed something on Google and ended up seeing related ads on Instagram or Facebook feed a while later. The creepy aspect of it put aside, this is how retailers are using machine learning applications to reach more relevant customers. Companies use big data from vendors and then send you ads that are relevant to your searches and needs. Chatbots used for customer engagement also use ML.

7.      Automotive

We are living many practical examples of machine learning in the automotive industry, without even knowing them. Here are some examples of technologies like:

  1. lane-change assist
  2. automatic breaking
  3. automatic parking
  4. smart energy management for extended mileage

Practically, a system learns about the variables of a certain 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.

8.      Space

Let’s just say that without the many applications of machine learning, modern space travel would simply be impossible. We want to learn about the harsh living conditions on our sister planets, but we cannot risk sending humans for the task. What we do is send machines equipped with machine learning algorithms. They control themselves and take on complex tasks by constantly crunching data.

Some examples of machine learning applications in space include:

  1. study of black holes,
  2. identifying invisible galaxies and clusters
  3. communication and navigation

All of these functions work on ML’s space-oriented knowledge.

9.      Entertainment Sector

Content creation is the best application of ML in the entertainment sector. There are so many projects that require the creation of vast realms, but it is simply impossible to bring them to life using standard 3D modeling.  It would take too much time and energy to complete manually. Thankfully, systems can now use machine learning to understand different elements of an environment and create logical iterations.

Conclusion

These are only a few of the many applications that machine learning has in today’s world. People are using it in many other industries as well and each of them is more exciting than the last. It is obvious 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 understand and appreciate the magnitude of machine learning applications.

 

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