H2O.ai

produces many tutorials, blog posts, presentations and videos about H2O, but the list below is comprised of awesome content produced by the greater H2O user community.

Using H2O AutoML to simplify training process (and also predict wine quality)

Aug 4, 2020

Visualizing ML Models with LIME

Parallel Grid Search in H2O

Jan 17, 2020

Importing, Inspecting and Scoring with MOJO models inside H2O

Dec 10, 2019

Artificial Intelligence Made Easy with H2O.ai: A Comprehensive Guide to Modeling with H2O.ai and AutoML in Python

June 12, 2019

Anomaly Detection With Isolation Forests Using H2O

Dec 03, 2018

Predicting residential property prices in Bratislava using recipes - H2O Machine learning

Nov 25, 2018

Inspecting Decision Trees in H2O

Nov 07, 2018

Gentle Introduction to AutoML from H2O.ai

Sep 13, 2018

Machine Learning With H2O — Hands-On Guide for Data Scientists

Jun 27, 2018

Using machine learning with LIME to understand employee churn

June 25, 2018

Analytics at Scale: h2o, Apache Spark and R on AWS EMR

June 21, 2018

Automated and unmysterious machine learning in cancer detection

Nov 7, 2017

Time series machine learning with h2o+timetk

Oct 28, 2017

Sales Analytics: How to use machine learning to predict and optimize product backorders

Oct 16, 2017

HR Analytics: Using machine learning to predict employee turnover

Sep 18, 2017

Autoencoders and anomaly detection with machine learning in fraud analytics

May 1, 2017

Building deep neural nets with h2o and rsparkling that predict arrhythmia of the heart

Feb 27, 2017

Predicting food preferences with sparklyr (machine learning)

Feb 19, 2017

Moving largish data from R to H2O - spam detection with Enron emails

Feb 18, 2016

Deep learning & parameter tuning with mxnet, h2o package in R

Jan 30, 2017

Big data in psychiatry and neurology, Chapter 11: A scalable medication intake monitoring system

Diane Myung-Kyung Woodbridge and Kevin Bengtson Wong. (2021)

Hands on Time Series with R

Rami Krispin. (2019)

Mastering Machine Learning with Spark 2.x

Alex Tellez, Max Pumperla, Michal Malohlava. (2017)

Machine Learning Using R

Karthik Ramasubramanian, Abhishek Singh. (2016)

Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI

Darren Cook. (2016)

Disruptive Analytics

Thomas Dinsmore. (2016)

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science

Bradley Efron, Trevor Hastie. (2016)

R Deep Learning Essentials

Joshua F. Wiley. (2016)

Spark in Action

Petar Zečević, Marko Bonaći. (2016)

Handbook of Big Data

Peter Bühlmann, Petros Drineas, Michael Kane, Mark J. van der Laan (2015)

Automated machine learning: AI-driven decision making in business analytics

Marc Schmitt. (2023)

Water-Quality Prediction Based on H2O AutoML and Explainable AI Techniques

Hamza Ahmad Madni, Muhammad Umer, Abid Ishaq, Nihal Abuzinadah, Oumaima Saidani, Shtwai Alsubai, Monia Hamdi, Imran Ashraf. (2023)

Which model to choose? Performance comparison of statistical and machine learning models in predicting PM2.5 from…

Padmavati Kulkarnia, V.Sreekantha, Adithi R.Upadhyab, Hrishikesh ChandraGautama. (2022)

Prospective validation of a transcriptomic severity classifier among patients with suspected acute infection and…

Noa Galtung, Eva Diehl-Wiesenecker, Dana Lehmann, Natallia Markmann, Wilma H Bergström, James Wacker, Oliver Liesenfeld, Michael Mayhew, Ljubomir Buturovic, Roland Luethy, Timothy E Sweeney , Rudolf Tauber, Kai Kappert, Rajan Somasundaram, Wolfgang Bauer. (2022)

Depression Level Prediction in People with Parkinson’s Disease during the COVID-19 Pandemic

) Hashneet Kaur, Patrick Ka-Cheong Poon, Sophie Yuefei Wang, Diane Myung-kyung Woodbridge. (2021)

Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in…

Alex Luna, Joel Bernanke, Kakyeong Kim, Natalie Aw, Jordan D. Dworkin, Jiook Cha, Jonathan Posner (2021).

Appendectomy during the COVID-19 pandemic in Italy: a multicenter ambispective cohort study by the Italian Society of…

Alberto Sartori, Mauro Podda, Emanuele Botteri, Roberto Passera, Ferdinando Agresta, Alberto Arezzo. (2021)

Forecasting Canadian GDP Growth with Machine Learning

Shafiullah Qureshi, Ba Chu, Fanny S. Demers. (2021)

Morphological traits of reef corals predict extinction risk but not conservation status

Nussaïbah B. Raja, Andreas Lauchstedt, John M. Pandolfi, Sun W. Kim, Ann F. Budd, Wolfgang Kiessling. (2021)

Machine Learning as a Tool for Improved Housing Price Prediction

Henrik I W. Wolstad and Didrik Dewan. (2020)

Citizen Science Data Show Temperature-Driven Declines in Riverine Sentinel Invertebrates

Timothy J. Maguire, Scott O. C. Mundle. (2020)

Predicting Risk of Delays in Postal Deliveries with Neural Networks and Gradient Boosting Machines

Matilda Söderholm. (2020)

Stock Market Analysis using Stacked Ensemble Learning Method

Malkar Takle. (2020)

H2O AutoML: Scalable Automatic Machine Learning

. Erin LeDell, Sebastien Poirier. (2020)

Single-cell mass cytometry on peripheral blood identifies immune cell subsets associated with primary biliary…

Jin Sung Jang, Brian D. Juran, Kevin Y. Cunningham, Vinod K. Gupta, Young Min Son, Ju Dong Yang, Ahmad H. Ali, Elizabeth Ann L. Enninga, Jaeyun Sung & Konstantinos N. Lazaridis. (2020)

Prediction of the functional impact of missense variants in BRCA1 and BRCA2 with BRCA-ML

Steven N. Hart, Eric C. Polley, Hermella Shimelis, Siddhartha Yadav, Fergus J. Couch. (2020)

Innovative deep learning artificial intelligence applications for predicting relationships between individual tree…

İlker Ercanlı. (2020)

An Open Source AutoML Benchmark

Peter Gijsbers, Erin LeDell, Sebastien Poirier, Janek Thomas, Berndt Bischl, Joaquin Vanschoren. (2019)

Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial…

Sebastian Raschka, Joshua Patterson, Corey Nolet. (2019)

Human actions recognition in video scenes from multiple camera viewpoints

Fernando Itano, Ricardo Pires, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandeza. (2019)

Extending MLP ANN hyper-parameters Optimization by using Genetic Algorithm

Fernando Itano, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandez. (2018)

askMUSIC: Leveraging a Clinical Registry to Develop a New Machine Learning Model to Inform Patients of Prostate Cancer…

Gregory B. Auffenberg, Khurshid R. Ghani, Shreyas Ramani, Etiowo Usoro, Brian Denton, Craig Rogers, Benjamin Stockton, David C. Miller, Karandeep Singh. (2018)

Machine Learning Methods to Perform Pricing Optimization. A Comparison with Standard GLMs

Giorgio Alfredo Spedicato, Christophe Dutang, and Leonardo Petrini. (2018)

Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions

Yuriy Kochura, Sergii Stirenko, Yuri Gordienko. (2017)

Algorithmic trading using deep neural networks on high frequency data

Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón. (2017)

Generic online animal activity recognition on collar tags

Jacob W. Kamminga, Helena C. Bisby, Duc V. Le, Nirvana Meratnia, Paul J. M. Havinga. (2017)

Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using…

Tomislav Hengl, Johan G. B. Leenaars, Keith D. Shepherd, Markus G. Walsh, Gerard B. M. Heuvelink, Tekalign Mamo, Helina Tilahun, Ezra Berkhout, Matthew Cooper, Eric Fegraus, Ichsani Wheeler, Nketia A. Kwabena. (2017)

Robust and flexible estimation of data-dependent stochastic mediation effects: a proposed method and example in a…

Kara E. Rudolph, Oleg Sofrygin, Wenjing Zheng, and Mark J. van der Laan. (2017)

Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition

Vincent Dorie, Jennifer Hill, Uri Shalit, Marc Scott, Dan Cervone. (2017)

Using deep learning to predict the mortality of leukemia patients

Reena Shaw Muthalaly. (2017)

Use of a machine learning framework to predict substance use disorder treatment success

Laura Acion, Diana Kelmansky, Mark van der Laan, Ethan Sahker, DeShauna Jones, Stephan Arnd. (2017)

Ultra-wideband antenna-induced error prediction using deep learning on channel response data

Janis Tiemann, Johannes Pillmann, Christian Wietfeld. (2017)

Inferring passenger types from commuter eigentravel matrices

Erika Fille T. Legara, Christopher P. Monterola. (2017)

Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500

Christopher Krauss, Xuan Anh Doa, Nicolas Huckb. (2016)

Identifying IT purchases anomalies in the Brazilian government procurement system using deep learning

Silvio L. Domingos, Rommel N. Carvalho, Ricardo S. Carvalho, Guilherme N. Ramos. (2016)

Predicting recovery of credit operations on a Brazilian bank

Rogério G. Lopes, Rommel N. Carvalho, Marcelo Ladeira, Ricardo S. Carvalho. (2016)

Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering

Ebberth L. Paula, Marcelo Ladeira, Rommel N. Carvalho, Thiago Marzagão. (2016)

Deep learning and association rule mining for predicting drug response in cancer

Konstantinos N. Vougas, Thomas Jackson, Alexander Polyzos, Michael Liontos, Elizabeth O. Johnson, Vassilis Georgoulias, Paul Townsend, Jiri Bartek, Vassilis G. Gorgoulis. (2016)

The value of points of interest information in predicting cost-effective charging infrastructure locations

Stéphanie Florence Visser. (2016)

Adaptive modelling of spatial diversification of soil classification units. Journal of Water and Land Development

Krzysztof Urbański, Stanisław Gruszczyńsk. (2016)

Scalable ensemble learning and computationally efficient variance estimation

Erin LeDell. (2015)

Superchords: decoding EEG signals in the millisecond range

Rogerio Normand, Hugo Alexandre Ferreira. (2015)

Understanding random forests: from theory to practice

Gilles Louppe. (2014)

Are categorical variables getting lost in your random forests?

Benchmark of categorical encoding schemes and the effect on tree based models (Scikit-learn vs H2O). Oct 28, 2016

Deep learning in R

Benchmark of open source deep learning packages in R. Mar 7, 2016

Szilard's machine learning benchmark

Benchmarks of Random Forest, GBM, Deep Learning and GLM implementations in common open source ML frameworks. Jul 3, 2015

Pipelines for model deployment

Apr 25, 2017

Machine learning with H2O.ai

Jan 23, 2017

University of San Francisco (USF) Distributed Data System Class (MSDS 697)

Master of Science in Data Science Program.

University of Oslo: Introduction to Automatic and Scalable Machine Learning with H2O and R

Research Bazaar 2019

UCLA: Tools in Data Science (STATS 418)

Masters of Applied Statistics Program.

GWU: Data Mining (Decision Sciences 6279)

Masters of Science in Business Analytics.

University of Cape Town: Analytics Module

Postgraduate Honors Program in Statistical Sciences.

Coursera: How to Win a Data Science Competition: Learn from Top Kagglers

Advanced Machine Learning Specialization.

modeltime.h2o R package

Forecasting with H2O AutoML

Evaporate

Run H2O models in the browser via Javascript. More info here.

splash R package

Splashing a User Interface onto H2O MOJO Files. More info here.

h2oparsnip R package

Set of wrappers to bind h2o algorthms with the parsnip package.

Spin up PySpark and PySparkling on AWS

Forecast the US demand for electricity

A real-time dashboard of the US electricity demand (forecast using H2O GLM)

h2o3-pam

Partition Around Mediods (PAM) clustering algorithm in H2O-3

h2o3-gapstat

Gap Statistic algorithm in H2O-3