Course Outline

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

Requirements

  • Experience with machine learning algorithms.
  • Python or R programming experience.

Audience

  • Data analysts
  • Data scientists
  • Data engineers
  • Developers
 14 Hours

Testimonials (5)

Related Categories