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What‌ ‌is‌ ‌Machine‌ ‌Learning?‌ ‌A‌ ‌Full‌ ‌Definition‌

What‌ ‌is‌ ‌Machine‌ ‌Learning?‌ ‌A‌ ‌Full‌ ‌Definition‌

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Machine learning technologies are permeating all areas of our lives. Artificial intelligence takes over more and more tasks instead of people. Self-driving cars, smart home appliances, and gadgets, contextual advertising – this no longer seems unusual to us. But until now, not everyone knows that all this exists thanks to machine learning.

Understanding machine learning definition is a process in which, in the course of solving a large number of similar problems, an analytical system identifies patterns and learns to make further decisions without human intervention. Simply put, machine learning technology is based on searching for patterns in the mass of information and choosing the best solution from the presented ones.

Thanks to machine learning consulting company AI constantly receives new problems and learns to solve them on its own. For example, in order to determine that a company’s voice robot should respond to a specific customer request, a computer must analyze thousands of requests, track changes in customer response with a particular response, and learn to independently choose the most suitable option in the future.

Uses of Machine Learning

Machine learning technology is most often used in marketing. For example, Amazon uses it to show customers the product that they should be interested in. It does this by analyzing data about past purchases and other users.

Google also uses machine learning to serve ads to specific users. If you have ever noticed that after searching for information about a product, you almost immediately saw the corresponding ad in search engines, then this was done thanks to machine learning technologies.

Smart feeds in social networks are arranged in the same way. Analytical systems Facebook, Instagram, Twitter or TikTok investigate the interests of users by all the data known about them: viewing posts, likes and comments, visiting publics and groups, etc. The higher the user’s activity, the more personalized feed AI selects for him.

Voice assistants like Alice and Siri or voice robots that answer you on the phone use speech recognition and synthesis systems that are also based on machine learning.

In addition, machine learning is used in medicine and security control structures. In medicine, this is a preliminary diagnosis and selection of an individual treatment plan based on data from the patient’s medical history. And in the field of security – face recognition systems. The machine compares images of people from CCTV cameras with photos of people on the wanted list. With a high resemblance, she gives a signal to the police.

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The main tasks that AI performs with the help of machine learning are:

  1. Regression. The system from the array of presented characteristics predicts the result in the form of a specific figure. For example, this way you can predict how much a Gazprom share will cost in a month or several years, as well as determine the budget for an advertising campaign, etc.
  2. Classification. The system determines the category of the analyzed object by a set of features. For example, you can identify spam in emails, or you can recognize which gender is in a photo.
  3. Clustering. The system divides the provided data array into categories. For example, requests to a company from customers can be divided into categories: by advertising sources, types of requests, etc.


Today, thanks to the development of the machine learning technologies of Applandeo, the programmer does not have to manually prescribe all possible problems and their solutions, now the program does it: a certain algorithm is put into it, according to which it independently finds solutions and makes predictions. Once upon a time, this was something of a fantasy for us, but artificial intelligence in the future can replace humans in many areas.

Thus, it is predicted that in the future robots will save people from fires, drill wells and explore the ocean floor, etc. This will eliminate the human factor and errors in the code since the system itself will learn and know how to behave in a specific situation.

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