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Machine Learning

Create systems that can learn, adapt and perform autonomously

Change or be changed

We define “disruptive” as the “ability of an algorithm to perform a cognitive task at a level sufficient enough to allow humans to accomplish tasks of greater value that machines are unable to perform”. In that sense, advanced analytical tools, such as deep learning, are the disruptive element par excellence.

Nologin and its experience, can allow you to make the qualitative leap in the analysis of your data that you need.

The technology already exists. Where do you want us to help your apply it?.

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What is and for what purpose is Machine Learning useful for?

The machines are very good at accomplishing repetitive tasks. Thanks to this, they can tirelessly sort through millions of data and are able to extract patterns of behavior, which human intelligence would take years to discover. These patterns, detected through the machine learning system, can allow us to predict results and behaviors in multiple sectors, such as the following:

Health care

AI will allow for earlier diagnosis and greater accuracy, helping make the impossible possible by advancing research on the fields in which is applied.

Social Media

Providers will be able to deliver a more personalized experience to their customers and offer more targeted reach to their advertisers.


The accelerated learning delivered in this new platform brings us another step closer to putting autonomous vehicles on the road.


Consider the immense data required to understand movement, wind speeds, water temperatures and other factors that decide the weather. Having a processor that takes better advantage of data inputs could improve predictions.


It is possible to analyze and increase the performance of the data obtained by quality systems, measurement and monitoring, allowing to determine in real time the quality of products manufactured according to their technical characteristics..

Banking and Insurance

In these sectors, it is vital to determine a client's degree of satisfaction, recommend new products and calculate the risk that the granting of loans, mortgages, policies... In these cases, having a model that learns from historical data is a great resource..

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