H2O is an open source, in-memory, distributed, fast and scalable machine learning and predictive analytics platform that allows you to build machine learning models on big data. Using in-memory compression, H2O handles billions of data rows in-memory, even with a small cluster. H2O is used by over 60,000 data scientists and more than 7,000 organizations around the world.
H2O includes a wide range of data science algorithms and estimators for supervised and unsupervised machine learning such as generalized linear modeling, gradient boosting, deep learning, random forest, naive bayes, ensemble learning, generalized low rank models, k-means clustering, principal component analysis, and others. H2O provides interfaces for Python, R, Java and Scala, and can be run in standalone mode or on a Hadoop/Spark cluster via Sparkling Water or sparklyr.
In this blog post, we’ll demonstrate you how you can install and use H2O with Python alongside the 720+ packages in Anaconda to perform interactive machine learning workflows with notebooks and visualizations as part of Anaconda’s Open Data Science platform.
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