Machine Learning

Deep Machine Learning Neural Networks

Machine Learning has its roots in statistical analysis. Many of its fundamental concepts were already well understood for more than a century. While statistics itself has seen a very gradual adoption as a viable tool for solving real-world problems, Machine Learning as an engineering discipline also had a rough false start in the late 80’s, giving the entire field of Artificial Intelligence a bad name. As it turns out, the reason Machine Learning as a specialty and statistics as an application discipline failed to deliver at first is less an indication of fundamental flaws, and more a consequence of two market conditions: not enough data, and not enough computing power. Both of these factors were to be erased within a few short decades.

Today, the Big Data juggernaut is rapidly revolutionizing the IT infrastructure landscape, and proven Machine Learning techniques are reshaping business practices in almost every nameable industry. Pundits in the Blogosphere are quick to declare Data Science the sexiest job on Earth. stands out as a thought leader in Machine Learning practices, delivering custom Predictive Modeling solutions to its clients on the same robust and scalable engineering platform that backs its software and integration offerings. We excel in a complete portfolio of Data Science expertise as enumerated below:

  • Supervised Learning – leverage subject matter expertise to label text and numerical data. Application of classical and Deep Learning techniques to highlight likely business outcomes and suggest the best course of action, aka Next Best Action (NBA).
  • Unsupervised Learning – classify a large collection of data (e.g., customer profile, product attributes, etc.) into clusters with identifiable characteristics. This is an important step in yielding human understanding of otherwise unwieldy data.
  • Classical Statistical Algorithms – linear regression models for predicting outcome based on selected attributes. Also provides part-worth analysis when companies design their products and services. Other complementary techniques include decision tree, random forest, support vector machine, latent class analysis, dirichlet allocation analysis, etc.
  • Data Sampling – predictions are as good as the data fed into the model. Modeling doesn’t start until data scientists sort out signals from the typical chaotic noise elements, using advanced sampling techniques such as generalized low ranking model, k-fold, bootstrap, jack knifing, dimension reduction, etc.
  • Super Learner – latest practice trend is favoring simultaneous applications of multiple models to find the most optimal prediction result. Super Learner is the concluding step which draws the best results from each component model.
  • Deep Neural Network – The class of problem with the highest data volume, dimensionality, and complexity automatically points to the Rolls Royce of Machine Learning, which is the Deep Neural Network. Capable of self-learning, non-linear transformations, and graduate improvement with each supply of new data.

Read more on three major Machine Learning areas fundamental to today’s data-driven enterprises: deep learning, complex event processing, and predictive modeling.

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