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Course Outline
Introduction
Setting Up the 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 with machine learning models.
- Background in Python programming is advantageous, though not mandatory.
Target Audience
- Data analysts
- Subject matter experts (domain experts)
- Data scientists
14 Hours
