H2O.ai created AI Self-Paced Courses out of inspiration for democratizing open source, distributed machine learning. Our self-paced courses are open to anyone in the community who would like to learn Distributed Machine Learning. The self-paced courses hosted here are targeted at people of all skill levels.
If you are certain there is an issue with the self-paced course, please create a new issue on Github, and we will do our best to resolve it.
For other Driverless AI versions of the self-paced courses visit our GitHub repository:
Driverless AI Self-Paced Courses Learning Path:
This self-paced course 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 self-paced course 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 Self-Paced Courses Learning Path:
This self-paced course 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.
MLOps Self-Paced Courses Learning Path: