Practical Uses for Machine Learning

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( — October 8, 2021) — Machine Learning is a subset of Artificial Intelligence. This technology has gained widespread use in a number of different industries, but despite this, many people may not know or understand the different practical uses for Machine Learning technology. 

Gaining a better understanding of how Machine Learning is being put to practical use on a daily basis can help your business take advantage of this technology during the app development process. Implementing Machine Learning in your business processes or applications is easiest during the development stage. However, you can still implement Machine Learning tools after the fact as well. 

Before we get too far ahead of ourselves, let’s explore practical, everyday uses for Machine Learning technology that you may not have considered. Perhaps, some of these capabilities will be useful to your business processes. 

Putting Machine Learning to Use

Machine Learning tools are able to hone their abilities by gathering or receiving data. The more information that these tools receive, the better they are at making predictions or decisions. Some of the most common Machine Learning uses are:

  • Image recognition
  • Medical diagnosis 
  • Predictive analytics 
  • Speech recognition 

Check out more details about these common uses, and see if your organization could benefit from using Machine Learning tools.

Image Recognition 

Image recognition is one of the most widely used and widely known uses of Machine Learning. Have you ever uploaded a picture to Facebook and then been given recommendations on which of your friends or family members to tag in the photo? This is an everyday example of Machine Learning. The more photos you upload and tag, the better Facebook’s image recognition tool gets at identifying the people in the picture. 

Image recognition is more capable than merely tagging photos. It can be used to read handwriting, analyze security footage for anomalies, and for facial recognition in law enforcement applications. 

As we will see below, Machine Learning image recognition technology can also be put to use in medical settings to analyze moles and other potentially worrisome spots on the skin. 

Medical Diagnosis 

An accurate and timely diagnosis can mean life or death for some patients. Machine Learning has been getting used extensively in the medical field. As we have already highlighted, Machine Learning is good at identifying potential skin cancer, but it is used for so much more than that. 

Machine Learning has had success in assisting in the formulation of a diagnosis and treatment plan based on medical records and test results. It can analyze bodily fluids to look for potential markers for issues that may not yet present themselves visually or on test results. Plus, it has been used to help make an early HIV diagnosis based on identifying predictors. 

Machine Learning tools are revolutionizing medical care and improving outcomes for patients. 

Predictive Analytics 

Businesses can always benefit from making better decisions. We can’t see the future, but with the assistance of Machine Learning tools, we can get more accurate predictions and make better decisions. 

This type of Machine Learning can be used to analyze past data like financial transactions to determine if a transaction is fraudulent. It can also be used to improve decision-making processes by predicting the statistical probabilities of outcomes. 

This can be applied to a wide variety of different fields, from dispute resolution to manufacturing and marketing. Realistically, using Machine Learning for predictive analytics is applicable to any business. 

These tools show a lot of promise, and when they are combined with human analysts, they are able to learn from the decisions humans make as well. Human analysts are also able to check the work done by a Machine Learning tool to make sure that the conclusions are sound. 

Speech Recognition

Speech recognition tools have been gaining a lot of popularity lately, with smart assistants like Alexa and Siri leading the charge. Alexa and Siri both rely on Machine Learning algorithms to perform the way that they do. 

Voice-enabled technology has gotten expensive. When it is paired with other smart technologies such as thermostats, fridges, and lighting fixtures, it has the ability to give users complete control over their home or office with voice commands. 

Speech recognition technology has also been used to transcribe audio recordings, turn voice into text, search the Internet, and make phone calls. These Machine Learning tools help users go hands-free. This makes things more accessible for people with disabilities and easier to handle for people with their hands full. 

Without Machine Learning, all of the great voice-enabled technologies we have come to enjoy and rely on would be useless. 

Final Thoughts 

Machine Learning and Artificial Intelligence might seem like esoteric topics far too complicated for your business. However, the truth is that these tools are already being widely adopted and used today. If you want to keep up with the rest of the world, you need to consider how you will harness the power of Machine Learning for your organization.