Scalable Machine Learning in R and Python with H2O

We have organized this event in collaboration with RLadiesBCN!!

Thanks also to Comissió d’Igualtat de la Facultat de Matemàtiques i Informàtica de la UB to make it possible.

https://a248.e.akamai.net/secure.meetupstatic.com/photos/event/f/b/3/600_456484019.jpeg

If you want to attend, please sign up in RLadies Meeetup event:

https://www.meetup.com/es-ES/rladies-barcelona/events/235593376/

The focus of this talk is scalable machine learning using the H2O R (http://www.h2o.ai/download/h2o/r) and Python (http://www.h2o.ai/download/h2o/python) packages. H2O (https://github.com/h2oai/h2o-3) is an open source, distributed machine learning platform designed for big data, with the added benefit that it's easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster). The core machine learning algorithms of H2O are implemented in high-performance Java, however, fully-featured APIs are available in R, Python, Scala, REST/JSON, and also through a web interface.

Since H2O's algorithm implementations are distributed, this allows the software to scale to very 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, Deep Neural Nets, dimensionality reduction methods (PCA, GLRM), clustering algorithms (K-means), anomaly detection methods, among others. The ability to create stacked ensembles, or "Super Learners", from a collection of supervised base learners is provided via the h2oEnsemble (https://github.com/h2oai/h2o-3/tree/master/h2o-r/ensemble) R package.

IMPORTANT INFORMATION:

R and Python code with H2O machine learning code examples will be demoed live and are available on GitHub (https://github.com/h2oai/h2o-tutorials#r-tutorials) for attendees to follow along on their laptops. So please bring a charged laptop if you want to try the code! Please also install the h2o package before coming (h2o package on CRAN, in Python pip install h2o)

## Speaker Bio

Erin LeDell (http://www.stat.berkeley.edu/~ledell/) is a Statistician and Machine Learning Scientist at H2O.ai (http://www.h2o.ai/), the company that produces the open source machine learning platform, H2O (https://github.com/h2oai/h2o-3). She is the author of a handful of machine learning related software packages (http://www.stat.berkeley.edu/~ledell/software.html), including the h2oEnsemble (https://github.com/h2oai/h2o-3/tree/master/h2o-r/ensemble) R package for ensemble learning with H2O. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from UC Berkeley. Before joining H2O.ai, she was the Principal Data Scientist at Wise.io (acquired by GE in 2016) and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc.