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Toolkits eases integration of machine learning to apps

06 Oct 2015  | Julien Happich

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"There is significant research focus on how the results of machine learning for one service could be transferred to another service, but the domains have to be related. What could work for sharing multimedia content would not make sense for your personal medical record."

The CEO doubts that different companies would want to share common profiles, at best companies could re-utilise what they have learned on a profile across different services.

"Machine learning offers an exciting opportunity but we need to make it accessible to more people" he concluded.

Whats in the toolkit?

Recommendation engines can be added to applications to personalise user experiences by mining patterns in purchase and activity history, matching users' tastes and predicting future purchases or interests. Examples include Pandora, MagazineLuiza and StumbleUpon.

Image search and feature extraction using deep learning can be used to create more intuitive search experiences, automatically tag photos and improve application performance by using highly predictive features extracted from images. Examples include Compology, Beeva and Zillow.

Churn prediction detects which customers are likely to cancel a subscription or service by using advanced analytics to detect specific patterns. It can be added to applications to identify which customers are at risk of "churning." Examples include PayPal and Nuiku.

Sentiment analysis uses natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. The technology can be used to discover the tone of a post in support forums or a customer's opinion of a brand or product in reviews for better targeting. Examples include Nook, Cisco and Moodly.

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