Machine Learning is a concept which deals with how to train a computer to 'discover and learn' patterns in a data set without being explicitly programmed for it. Over the last decade, there has been a tremendous proliferation of datasets which provide raw data for correlation analysis. A typical Machine Learning framework is provided with a large amount of data and is tasked with discovering patterns within it. To help it, one might choose to label the 'input' data. For example, we might give it 10,000 images of faces and label which ones are men and which ones are women. We call this 'supervised learning'. The system then proceeds to figure out patterns and relationships that converge to making sure it is accurately able to relate these patterns to the labels provided. In this approach, the system is provided with a 'correct dataset' to learn in the hope that the learning from this 'training set' can be applied to 'unknown but similar' data sets in the future. Another example is how Bayesian filters work on email spam - we users keep flagging emails as spam which it uses as a learning tool (training data) to be able to mark future emails as spam.
Another form is to provide the system with a large amount of data and asking it to figure our relationships without guidance. This is called 'unsupervised learning' An example of this could be 'here is 5 years worth of data for what a section of users search for on the internet. 5 years later these users had cancer. Use this data to find out if we can identify key common early symptoms of cancer, if possible'. (This was not a random example, Microsoft Research did something similar)
HSC works with solution providers in the Retail, IoT and Communications vertical to develop custom machine learning models for our customers where we process massive amounts of data and try to categorize and create trends so we can understand and interpret the data. We also help our customers create 'prediction models' that drive specific outcomes.
Some of the key Machine Learning frameworks HSC uses:
In addition to working on machine learning modeling, HSC also works on traditional targetted algorithm based Big Data systems using tools such as:
Disclaimer: Hughes Systique shall not be liable for any loss or damage sustained by reason of any disclosure (inadvertent or otherwise) of any information concerning the user's account nor for any error, omission or inaccuracy with respect to any information so disclosed and used whether in pursuance of a legal process or otherwise.
Any other personal information (including sensitive personal information) shared by you which is not asked by Hughes Systique during registration, either mandatorily or optionally; accounts to wilful and intentional furnishing; and Hughes Systique will not be liable for breach of such information.
These cookies are necessary for the website to function and cannot be switched off.
These cookies allow us to monitor traffic to our website so we can improve the performance and content of our site. They help us to know which pages are the most and least popular and see how visitors move around the site. All information these cookies collect is aggregated and therefore anonymous. If you do not allow these cookies we will not know when you have visited or how you navigated around our website.
These cookies enable the website to provide enhanced functionality and content. They may be set by the website or by third party providers whose services we have added to our pages. If you do not allow these cookies then some or all of these services may not function properly.
These cookies may be set through our site by our advertising partners. They may be used by those companies to build a profile of your interests and show you relevant adverts on other sites. They do not store directly personal information, but are based on uniquely identifying your browser and internet device. If you do not allow these cookies, you will experience less targeted advertising.