Course Outline

Introduction

Setting up H2O

Overview of H2O Features and Architecture

Navigating the H2O WebUI

Preparing the Dataset

Working with Decision Tree Models

Creating a Linear Model

Real-time Data Scoring in H2O

Creating a Random Forest Model

Creating GBMs

Analyzing Hadoop Data 

Creating a Deep Learning Model

Creating an Unsupervised Learning Model

Using H2O AutoML to Automate the Model Evaluation Process

Troubleshooting

Summary and Conclusion

Requirements

  • Programming experience in Python, R, Scala, or Java.

Audience

  • Data scientists
  • Data analysts
  • Developers
 14 Hours

Testimonials (5)

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