Intro to Machine Learning with H2O and Python

Event Details

Intro to Machine Learning with H2O and Python

Time: March 1, 2016 from 5:30pm to 7:30pm
Location: Comcast
Street: 1899 Wynkoop, 5th floor
City/Town: Denver, CO
Website or Map: http://www.meetup.com/Data-Sc…
Event Type: free, and, open, to, all
Organized By: Michael Walker
Latest Activity: Mar 1, 2016

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Event Description

Free livestream registration here.

This workshop will provide an overview of how to use H2O, the scalable open source machine learning library, from Python/R/Flow UI. The core algorithms of H2O are implemented in Java, however, fully-featured APIs are available in R, Python, Scala, and also through the Flow UI web interface. The focus of this hands-on workshop will be the “h2o” R & Python modules. All of H2O's algorithm implementations are distributed, which allows the software to scale to large datasets that may not fit into RAM on a single machine. H2O currently features distributed implementations of Generalized Linear Models, Gradient Boosting Machines, Random Forest and Deep Neural Nets. In this workshop, attendees will learn how to train machine learning models, cross-validation, and evaluate model performance using the H2O Python and R API. Meet the Speaker:

Spencer Aiello is an engineer at H2O and is responsible for the designs and implementations of the Python and R interfaces. In addition to the front-end interfaces, Spencer also helps to maintain the lower-level distributed big data frame operations that these interfaces depend upon. His interests range from distributed computing to API designs for data science.

Tom Kraljevic is VP of Engineering at H2O. Before joining H2O, Tom was Co-founder & CTO at Luminix, where he and the team developed a cutting-edge offline mobile application for Salesforce users. This involved a healthy blend of focusing on the user-experience along with a deep-dive in various technologies. Prior to Luminix, Tom was a Principal Engineer at Azul Systems, where he worked in both the JVM and System Software teams. Tom served as the technical leader for the distributed management application team, appliance security and tools for distributed debugging. Tom’s experience at systems and chip startup companies involved straddling the hardware-software boundary.

Nick Karpov is a Customer Success and Experience Hacker. Nick comes from a Computer Engineering background and is new to the Data Science community. He works with H2O’s customers to deploy, manage, and drive H2O usage in production.

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