Hollin Wilkins discusses the reasons behind MLeap, outes the programming time saved by using it, shows benchmarks of several online models, and provides a demo and examples of using it in practice.
James Weaver takes a deeper dive into machine learning topics such as supervised learning, unsupervised learning, and deep learning, surveying various machine learning APIs and platforms.
Alok Aggarwal overviews Artificial Intelligence and discusses a use case, “Voice of Cancer Patients” that uses ML and NLP algorithms to analyze unstructured text written by cancer patients.
Danny Lange presents Uber’s Machine Learning service that can perform functions such as ETA, fraud detection, churn prediction, forecasting demand, and much more.
Ian Fyfe discusses the different options for implementing speed-of-thought business analytics and machine learning tools directly on top of Hadoop.
Ali Jalali presents how to develop a machine learning predictive analytics engine for big data analytics.
Simon Chan shares the on-going challenges, the design dilemma and the steps to be taken when building customized large-scale predictive ML applications on a ML SaaS platform.
Suman Deb Roy talks about some of Betaworks’ internal data tools and platform, product-specific solutions and best practices they learned when machine learning has to drive the startup road.
John Langford discusses how to use Vowpal Wabbit in and as a machine learning system including architecture, unique capabilities, and applications, applied to personalized news recommendation.
Edo Liberty presents some basic concepts and an introduction to the subfields of machine learning and data mining.
David Talby demos using Python libraries to build a ML model for fraud detection, scaling it up to billions of events using Spark, and what it took to make the system perform and ready for production.
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.