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• Introduction
• Setting up a Working Environment
• Installing H2O
• Anatomy of a Standard Machine Learning Workflow
• Data-preprocessing, feature engineering, deployment， etc.
  Statistical and Machine Learning Algorithms
• Gradient boosted machines, generalized linear models, deep learning, etc.
  How H2O Automates the Machine Learning Workflow
• Binary Classification, Regression, etc.
  Case Study: Predicting Product Availability
• Downloading a Dataset
• Building a Machine Learning Model
• Specify a Training Frame
• Training and Cross-Validating Different Models
• Tuning the Hyperparameters
• Training two Stacked Ensemble Models
• Generating a Leaderboard of the Best Models
• Inspecting the Ensemble Composition
• Training a Large Number of Deep Neural Network Models
• Troubleshooting
• Summary and Conclusion