Getting Started with H20.ai

This course will familiarize you with different recipes of H2O’s Driverless AI. You'll learn to build a fully automated ML pipeline, with built-in feature engineering, feature transformations, automatic visualizations, and inference mechanisms.

Would you like to build end to end ML Pipelines using H2O Driverless AI? In this course, Getting Started with H2O.ai, you’ll learn to do ML predictive modelling using built-in classification/regression algorithms. First, you’ll explore how to pull in data sets from multiple sources like S3, FileSystem, databases, etc. Next, you’ll discover correlation patterns based on a bunch of visualization methods available within. Finally, you’ll learn how to do feature engineering/transformations, outlier detection, and training based on multiple tuning knobs available like score, interpretability, and accuracy. When you’re finished with this course, you’ll have the skills and knowledge of leveraging capabilities of H2O’s driverless AI in order to build predictive pipelines from scratch to production ready.

• Course Overview 1min
• Onboarding Datasets to H2O Driverless AI 15mins
• Visualizing Data Patterns Based on Input Features 14mins
• Implementing Feature Engineering and Transformations 14mins
• Predicting the Results from Trained Models 18mins
• Analyzing Predicted Results Based on Parameter Tuning 22mins