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Setting up a Working Environment
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
Summary and Conclusion
- Experience working with machine learning models.
- Python programming experience is helpful but not necessary.
- Data analysts
- Subject matter experts (domain experts)
- Data scientists
Google Cloud AutoML
AutoML with Auto-sklearn