H2O AutoML培訓

Introduction

Setting up a Working Environment

Installing H2O

Anatomy of a Standard Machine Learning Workflow

• Data-preprocessing, feature engineering, deployment， etc.

Statistical and Machine Learning Algorithms

• Gradient boosted machines, generalized linear models, deep learning, etc. 

How H2O Automates the Machine Learning Workflow

• Binary Classification, Regression, etc.

Case Study: Predicting Product Availability

Downloading a Dataset

Building a Machine Learning Model

Specify a Training Frame

Training and Cross-Validating Different Models

Tuning the Hyperparameters

Training two Stacked Ensemble Models

Generating a Leaderboard of the Best Models

Inspecting the Ensemble Composition

Training a Large Number of Deep Neural Network Models

Troubleshooting

Summary and Conclusion