Big Data

Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set.

Big Data

Natural Language Processing

Our data experts are the best in Natural language processing. We build systems that deals with analyzing, understanding & identifying data from languages that humans use. We have built NLP systems for Advertising, Healthcare & Retail domain.

Our stack
OpenNLP StanfordNLP LingPipe GATE Clips Natural Language Toolkit (NLTK) Custom Developed Algorithms

Machine Learning

We develop systems that solve your complex business problems using our machine learning techniques. With the strong knowledge of algorithms, math geniuses and data engineers put their brains together in identifying the solution and build them to make it awesome. From recommendation engines to spam prediction systems to social media analytics, we have powerful engines built upon ML techniques.

Our stack
Weka Apache Mahout MLLib LibLinear and LibSVM Custom Developed Algorithms
Our Expertise
Recommendation system Spam Prediction Fraud Detection Social Media Analytics
dexlock

Deep Learning

One of the challenges in using traditional machine learning techniques is the choice of features. For humans to analyse data and recommend a solution, it generally takes few days. The analysis might require plotting software that can plot in an n-dimensional space. Identifying the features is generally the most important step in traditional machine learning. However this becomes difficult as we move towards training datasets that contains multimedia. Our approach to such situations is to use an unsupervised technique. Off late we tend to use Deep Learning based machines more often than not. We have utilised toolkits such as Caffe, DeepLearning4J, Torch etc. This way we are able to make the machine derive the best features and learn by itself, take feedback and then learn again. Some of the deep learning networks we have used in the past include Restricted Boltzmann Machines, Deep Belief Networks, Convolutional Networks etc. We have successfully used these machines to solve problems such as Image classification, Video classification, Speech recognition, handwriting recognition etc.

Our Expertise