Supervised learning is typically done in the context of classification, when we want to map input to output labels, or regression, where we want to map input to a continuous output. Common algorithms that we implement in supervised learning include logistic regression, naive bayes, support vector machines, artificial neural networks, random forests, and ensemble models such as extreme gradient boosting. The accuracy of the model is compared using each of the methods used, and the best model of all is selected. However, our work does not stop here. We also build a pipeline to improve the model using new data that the model sees, so that you have a continuously improving and upto date machine learning model.
We use statistical modelling methods to detect, analyze and make inferences about patterns and relationships within data to support business decisions. We also offer insights into the main statistical methodologies used for modelling relationships in both discrete and continuous business data. In short, we make meaningful models for a range of specific tasks such as financial asset valuation, market research, demand and sales forecasting, and financial analysis among others, using modern software tools.
Over the past few years, deep learning has emerged as one of strongest tools of AI researchers which can be applied to automation, speech recognition and object classification in images. Our deep learning experts can help you with using this technology to lay the groundwork for future business strategy including details on state of the art products/services. Our core stack includes Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.
We use unsupervised learning in situations where the data is not labeled, to perform an exploratory analysis using clustering methods such as K Means Clustering, or reduce the dimensionality of the data using PCA, and autoencoders.
We provide industry-specific data mining services ranging from real estate, insurance, healthcare to finance, manufacturing and law firms. Our methodology encompasses gathering the data set requirement from the client, sourcing & collecting relevant data, output the data in the client’s required format, perform quality checks and deliver the final data set to the client.
Our predictive models provide accurate forecasts and reliable business projections to organizations who partner with us. We leverage the potential of modern predictive analytical tools to generate detailed insight and augment intelligence into business areas that reap maximized ROI and utmost productivity and profitability. We build these predictive models using supervised / unsupervised learning techniques based on the problem description at hand.