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Setting up a Working Environment
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 many Deep Neural Network Models
Summary and Conclusion
- Experience working with machine learning models.
- Python or R programming experience.
- Data scientists
- Data analysts
- Subject matter experts (domain experts)
AutoML with Auto-Keras
Google Cloud AutoML
AutoML with Auto-sklearn