H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

Tutorials and training material for the H2O Machine Learning Platform

Sparkling Water provides H2O functionality inside Spark cluster

This project contains examples which demonstrate how to deploy analytic models to mission-critical, scalable production environments leveraging Apache Kafka and its Streams API. Models are built with Python, H2O, TensorFlow, Keras, DeepLearning4 and other technologies.

A curated list of gradient boosting research papers with implementations.

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

H2O.ai Machine Learning Interpretability Resources

Presentations from H2O meetups & conferences by the H2O.ai team

Access log profiler based on response time

A curated list of research, applications and projects built using the H2O Machine Learning platform

Materials for GWU DNSC 6279 and DNSC 6290.

Comparison tools

R package for automation of machine learning, forecasting, feature engineering, model evaluation, model interpretation, data generation, and recommenders.

Identifying diseases in chest X-rays using convolutional neural networks

RSparkling: Use H2O Sparkling Water from R (Spark + R + Machine Learning)

Deep Learning UDF for KSQL, the Streaming SQL Engine for Apache Kafka with Elasticsearch Sink Example

DEPRECATED Build, manage and deploy H2O's high-speed machine learning models.

Showcase for using H2O and R for churn prediction (inspired by ZhouFang928 examples)

Showcase for using H2O and R for scoring for marketing campaign in retail

ForestFlow is a policy-driven Machine Learning Model Server. It is an LF AI Foundation incubation project.

Mercury-ML is an open source Machine Learning workflow management library. Its core contributors are employees of Alexander Thamm GmbH

docker files for h2o http2 webserver

A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health in Python

NOTE: skutil is now deprecated. See its sister project: https://github.com/tgsmith61591/skoot. Original description: A set of scikit-learn and h2o extension classes (as well as caret classes for python). See more here: https://tgsmith61591.github.io/skutil

Statistical Learning & Data Mining IV - H2O Presenation & Tutorial

Python binding for the H2O HTTP server

Exemplary, annotated machine learning pipeline for any tabular data problem.