View of Scalable Machine Learning in R and Python with H2O Seminar Boston AI & Deep learning Meetup The focus of this presentation is scalable machine learning using the h2o R and Python packages. H2O 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, Stacked Ensembles (aka "Super Learners"), dimensionality reduction methods (PCA, GLRM), clustering algorithms (K-means), anomaly detection methods, among others. R and Python code with H2O machine learning code examples will be demoed live and will be made available on GitHub for participants to follow along on their laptops if they choose. For those interested in running the code on a multi-node Amazon EC2 cluster, an H2O AMI is also available.