ISAX is a time series data compression algorithm that can group similar patterns in billions of time series datasets. It is implemented on H2O's distributed architecture and can be used for clustering, classification, anomaly detection, and predictive analytics on compressed time series data from fields like IoT, finance, bioinformatics, and image/sound processing. Examples of ISAX code in H2O are provided.
Erin LeDell's presentation on Intro to H2O Machine Learning in Python at Data Science LA meetup on 1.19.16 - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Talk given at Strata London with Dean Wampler (Lightbend) about Scala as the future of Data Science. First part is an approach of how scala became important, the remaining part of the talk is in notebooks using the Spark Notebook (http://spark-notebook.io/). The notebooks are available on GitHub: https://github.com/data-fellas/scala-for-data-science.
Erin LeDell presents Intro to H2O Machine Learning in Python at Galvanize Seattle, 02.02.16 - Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai - To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata