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Course Outline
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
Requirements
- Experience with machine learning algorithms.
- Python or R programming experience.
Audience
- Data analysts
- Data scientists
- Data engineers
- Developers
14 Hours
