Machine Learning

Algorithms to detect patterns and help your systems learn

Turn your data into competitive advantage

Machine Learning algorithms help your systems learn by detecting patterns in historical data and outcomes, making predictions and prescriptions based on that data, and adapting to new data for continually improved performance over time. The business value can be profound.

  • Enhanced market forecasting
  • Optimized pricing and load balancing
  • Proactive business and system security

Our Data Scientists and Machine Learning Engineers follow mature and predictable processes to help you understand and realize the potential of supervised, unsupervised, and reinforced Machine Learning for your organization.

Supervised Learning

To predict behavior based on the relationship between inputs to outputs. Supervised learning is helpful in such areas as fraud detection, market and customer segmentation, pattern or face recognition, image classification, market forecasting, and advertising popularity predictions. Algorithms: Linear Regression, Decision Tree, Random Forest, Logistic Regression.

Unsupervised Learning

To find patterns and classify the data without an explicit output variable. Unsupervised learning is helpful in recommendation systems, customer segmentation, spam filtering, news classification, social network analysis, search result grouping, image segmentation, anomaly detection, Big Data visualization, feature elicitation, and email classification. Algorithms: K-Means Clustering, Hierarchical Clustering, Gaussian Mixture Model.

Reinforced Learning

To perform a task by maximizing the rewards received. Reinforced learning can be helpful, for example, in optimizing pricing systems, optimizing trading strategies, and load balancing. Algorithms: Markov Decision Processes, Temporal Difference, Policy Gradient, Value Function, Q-Learning, SARSA.

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