H2O.ai created AI Tutorials out of inspiration for democratizing open source, distributed machine learning. Our tutorials are open to anyone in the community who would like to learn Distributed Machine Learning. Tutorials housed in our new H2O.ai Learning Center are targeted at people of all skill levels. Earn badges by completing the Self-Paced Tutorials or Instructor-Led Courses
If you run into problems that prevent you from completing a tutorial, head on over to H2O.ai Community (Hac) and chat with Data Scientists from all over the world. If you are certain there is an issue with the tutorial, please create a new issue on Github, and we will do our best to resolve it.
Interested in contributing updates or new tutorials? Fix issues, help clarify topics, update a tutorial to be compatible with the newest releases, or even create a brand new tutorial!
For other Driverless AI versions of the tutorials visit our GitHub repository:
Driverless AI Tutorials Learning Path:
This tutorial is for Driverless AI; You'll explore how to: evaluate a Driverless AI model through tools like, ROC, Prec-Recall, Gain and Lift Charts, K-S Chart as well as metrics such as AUC, F-Scores, GINI, MCC, and Log Loss.
This tutorial is for Driverless AI; You'll explore how to: launch an experiment, create ML Interpretability report, explore explainability concepts such as Global Shapley, partial dependence plot, decision tree surrogate, K-LIME, Local Shapley, LOCO and individual conditional expectation.
H2O-3 Tutorials Learning Path:
This tutorial is for H2O-3; you will learn how to solve a binary classification problem, explore a regression use-case, Automatic Machine Learning (AutoML), and we will do so using the H2O Python module in a Jupyter Notebook and also in Flow.