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• Introduction
• Setting up a Working Environment
• Installing Auto-sklearn
• Anatomy of a Standard Machine Learning Workflow
• How Auto-sklearn Automates the Machine Learning Workflow
• Searching for the Best Neural Network Architecture with NAS (Neural Architecture Search)
• Case Study: AutoML with Auto-sklearn
• 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-sklearn for Deep Learning Models
• Troubleshooting
• Summary and Conclusion