info@Qcentrio.com
 This is to notify to the public that some unscrupulous persons posing themselves as employees/representatives/agents of Qcentrio Inc. with ulterior motive to earn wrongful gain and/or cheat the prospective job seekers and students, are fraudulently offering jobs online on certain websites/or through telephone calls or fake employment letters, soliciting them for jobs in Qcentrio, and are asking them to deposit some amount in certain bank accounts. These people are also unauthorizedly using the name, trademark, domain name and logo of Qcentrio with a view to tarnish the image and reputation of Qcentrio. The public in general are also advised not to fall prey to such fraudulent activities and verify the domain of the email that they receive and act accordingly. Qcentrio do not ask, solicit and accept any monies in any form from the candidates/job applicants/potential job seekers, who have applied or wish to apply to us, whether online or otherwise as a pre-employment requirement. Qcentrio bears no responsibility for amounts being deposited / withdrawn therefrom in response to such Offers. Anyone dealing with such fake interview calls would be doing so at his / her own risk and the Qcentrio will not be held responsible for any loss or damage suffered by such persons, directly or indirectly. The company strongly recommends that the potential jobseekers should not respond to such solicitations. Should you come across any such fraudulent incident or have any information regarding solicitation for recruitment or employment with Qcentrio, please assist us in taking appropriate action to curb such mala fide activities. You may reach us at info@qcentrio.com.

Machine Learning

Machine learning solutions help in minimizing downtime, outage prediction, identifying outliers and trends before they occur, and preparing for fluctuations in production demands. We will integrate and deploy such solutions that will not only keep you online but will also continue learning about how your organization data flows, thus making itself optimized by its own to increase efficiency.

Build automated solutions that will help your business to increase its throughput and work on what is more important instead of doing repetitive tasks. We build intelligent systems that will improve data integrity and security protocols used in desktop, mobile or web applications.

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.


Evolution of machine learning


Because of new computing technologies, machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.


While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Here are a few widely publicized examples of machine learning applications you may be familiar with:



  • The heavily hyped, self-driving Google car? The essence of machine learning.
  • Online recommendation offers such as those from Amazon and Netflix? Machine learning applications for everyday life.
  • Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.
  • Fraud detection? One of the more obvious, important uses in our world today.
  • Data preparation capabilities.
  • Algorithms – basic and advanced.
  • Automation and iterative processes.
  • Scalability.
  • Ensemble modeling.

Did you know?


  • In machine learning, a target is called a label.
  • In statistics, a target is called a dependent variable.
  • A variable in statistics is called a feature in machine learning.
  • A transformation in statistics is called feature creation in machine learning.