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• 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