Course Outline

Introduction

Setting up a Working Environment

Installing Auto-Keras

Anatomy of a Standard Machine Learning Workflow

How Auto-Keras Automates the Machine Learning Workflow

Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)

Case Study: AutoML with Auto-Keras

Downloading a Dataset

Building a Machine Learning Model

Training and Testing the Model

Tuning the Hyperparameters

Building, Training, and Testing Additional Models

Tweaking the Hyperparameters to Improve Accuracy

Configuring Auto-Keras for Deep Learning Models

Troubleshooting

Summary and Conclusion

Requirements

  • Experience working with machine learning models.
  • Python programming experience is helpful but not necessary.

Audience

  • Data analysts
  • Subject matter experts (domain experts)
  • Data scientists
 14 Hours