Welcome to H2O.ai Self-Paced Courses

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:

Driverless AI Tutorials Learning Path

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Getting Started with Driverless AI Test Drive

Test Drive is a two-hour lab session that exists in H2O's Aquarium, a cloud environment that provides access to various tools for workshops, conferences, and training.
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Automatic Machine Learning Intro with Driverless AI

This self-paced course is for Driverless AI Test Drive; You'll explore how to: auto Visualize a dataset, launch an experiment, perform feature engineering and create ML Interpretability report.
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Machine Learning Experiment Scoring and Analysis - Financial Focus

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.
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Machine Learning Interpretability

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.
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Time Series - Retail Sales Forecasting

This self-paced course is for Driverless AI; you will learn what a time series experiment is, concepts behind time series, how to set a time series experiment on Driverless AI, time series MLI and more.
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Natural Language Processing - Sentiment Analysis

This self-paced course is for Driverless AI; you will learn how to launch a sentiment analysis experirement, walk through sentiment analysis experiment settings, NLP Concepts,Driverless AI NLP Recipe and more.
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Image Processing in Driverless AI

This self-paced course is for Driverless AI; You'll will explore the two different approaches to modeling images in Driverless AI: Embeddings Transformer (Image Vectorizer) and Automatic Image Model.
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Get Started with Open Source Custom Recipes

This self-paced course is for Driverless AI; you will learn what a recipe is, the types of open source recipes that are available on Driverless AI 1.9.1 and how to load and run them in experiments.
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Build Your Own Custom Recipe

This self-paced course is for Driverless AI; you will learn how to build and troubleshoot your own custom transformer, scorer and model recipes.
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Scoring Pipeline Deployment Introduction

This self-paced course is for Driverless AI; you will predict the cooling condition for a Hydraulic System Test Rig by deploying a MOJO Scoring Pipeline into production using Driverless AI.
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Scoring Pipeline Deployment Templates

This self-paced course is for Driverless AI; you will predict the cooling condition for a Hydraulic System Test Rig by deploying a MOJO Scoring Pipeline into production using Driverless AI's Production Ready Deployment Templates.
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Scoring Pipeline Deployment in Java Runtime

This self-paced course is for Driverless AI; you will predict the cooling condition for a Hydraulic System Test Ring by deploying an embeddable MOJO Scoring Pipeline into JavaRuntime using Java, Sparkling Water, and PySparkling.
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Scoring Pipeline Deployment in C++ Runtime

This self-paced course is for Driverless AI; you will predict the cooling condition for a Hydraulic System Test Rig by deploying an embeddable MOJO Scoring Pipeline into C++ Runtime using Python and R.
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Scoring Pipeline Deployment in Python Runtime

This self-paced course is for Driverless AI; you will predict the cooling condition for a Hydraulic System Test Rig by deploying a Python Scoring Pipeline from Driverless AI.
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Disparate Impact Analysis

This self-paced course is for Driverless AI; you will explore how to use the Disparate Impact Analysis tool to check for Bias.
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Disparate Impact Analysis

This self-paced course is for Driverless AI; you will explore how to use the Disparate Impact Analysis tool to check for Bias.
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Risk Assessment Tools in the World of AI, the Social Sciences, and Humanities

This self-paced course is for Driverless AI; learn how the social sciences, humanities, and AI should work together to create a more fair integration of AI into our society.

H2O-3 Self-Paced Courses Learning Path:

H2O Self-Paced Courses Learning Path

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Introduction to Machine Learning with H2O-3 - Classification

This self-paced course is for H2O-3; you will learn how use H2O's GLM, Random Forest, GBM models and grid search to tune hyperparementers for a classification problem.
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Introduction to Machine Learning with H2O-3 - Regression

This self-paced course is for H2O-3; you will learn how to used H2O’s XGBoost and Deep Learning models for a regression problem followed by H2O’s grid search to tune the hyper-parameters of both models.
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Introduction to Machine Learning with H2O-3 - AutoML

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:

MLOps Self-Paced Courses Learning Path

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Getting Started with MLOps Test Drive

This is a step by step guide to help you set-up your MLOps environment
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Intro to ML Model Deployment and Management

This self-paced course is for MLOps; you will create a Driverless AI project, share it with MLOps, and then deploy a Machine Learning model using MLOps.