Where Machine Learning can be used?
Machine learning is gradually finding a presence in most aspects of life, and artificial intelligence is demonstrating new technical achievements.
Vanda Baranova

Machine learning will not only assist in improving the quality of human existence in the near future, but they may also create technical revolutions in several fields of business and industry.

Machine learning is steadily entering a wide range of businesses, and computers and artificial intelligence systems are becoming capable of handling a wide range of assignments. Let’s have a look at how different businesses can adopt it in unexpected spheres of activity.


Diagnosis of diseases at an early stage

Machine learning aids in the identification of difficult-to-diagnose disorders, such as hereditary diseases or various forms of cancer that are difficult to detect in the early stages.

Medical images are most often used for analysis using models: MRI, CT, and fluorography.

It is important to mention that ML systems cannot diagnose patients and everything that is developed in the field of machine learning is aimed exclusively at facilitating the work of medical staff.


Quality control

In this field, machine learning algorithms enable employees to examine how even slight modifications not noticed by human workers might impact the outcome of creating particular goods and give recommendations.

Meanwhile, these recommendations can have a certain impact on the process of production.


Fintech and banking

In this field, machine learning is currently used to make many judgments. For example, the algorithm can aid in the resolution of credit rating issues. After analyzing consumer data from the questionnaire and open sources, the algorithm determines if it is worthwhile to make a loan to the client. This assists management in making choices and reducing risks for the bank.

When a client’s card is stolen and used to withdraw money, ML models may follow payments and identify fraudulent ones among them.


Autonomous vehicles

Machine learning algorithms allow autonomous cars to make decisions in real-time.

However, the challenge of this method is that the cost of error can be extremely high. When developers understand how to tackle certain obstacles, connected with safety and road accidents, self-driving cars will be seen on streets all over the world.


Thus, research and use of machine learning technology appears to be extremely reasonable and practical, since its value and relevance are expanding day by day.