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Course Outline
Introduction and Environment Setup
- What is AutoML and why it matters
- Setting up Python and R environments
- Configuring remote desktop and cloud environments
Exploring AutoML Features
- Core capabilities of AutoML frameworks
- Hyperparameter optimization and search strategies
- Interpreting AutoML outputs and logs
How AutoML Selects Algorithms
- Gradient Boosting Machines (GBMs), Random Forests, GLMs
- Neural networks and deep learning backends
- Trade-offs: accuracy vs. interpretability vs. cost
Data Preparation and Preprocessing
- Working with numeric and categorical data
- Feature engineering and encoding strategies
- Handling missing values and data imbalance
AutoML for Different Data Types
- Tabular data (H2O AutoML, auto-sklearn, TPOT)
- Time-series data (forecasting and sequential modeling)
- Text and NLP tasks (classification, sentiment analysis)
- Image classification and computer vision (Auto-Keras, TensorFlow, PyTorch)
Model Deployment and Monitoring
- Exporting and deploying AutoML models
- Building pipelines for real-time prediction
- Monitoring model drift and retraining strategies
Ensembling and Advanced Topics
- Stacking and blending AutoML models
- Privacy and compliance considerations
- Cost optimization for large-scale AutoML
Troubleshooting and Case Studies
- Common errors and how to fix them
- Interpreting AutoML model performance
- Case studies from industry use cases
Summary and Next Steps
Requirements
- Experience with machine learning algorithms
- Python or R programming experience
Audience
- Data analysts
- Data scientists
- Data engineers
- Developers
14 Hours
