         AutoML培训

Introduction

Setting up a Working Environment

Overview of AutoML Features

How AutoML Explores Algorithms

Gradient Boosting Machines (GBMs), Random Forests, GLMs, etc.
Solving Problems by Use-Case

Solving Problems by Training Data Type

Data Privacy Considerations

Cost Considerations

Preparing Data

Working with Numeric and Categorical Data

IID tabular data (H2O AutoML, auto-sklearn, TPOT)
Working with Time Dependent Data (Time-Series Data)

Classifying Raw Text

Classifying Raw Image Data

Deep Learning and Neural Architecture Search (TensorFlow, PyTorch, Auto-Keras, etc.)
Deploying an AutoML Method

A Look at the Algorithms Inside AutoML

Ensembling Different Models Together

Troubleshooting

Summary and Conclusion