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Course Outline
Introduction and Environment Setup
- Understanding AutoML and its significance
- Configuring Python and R environments
- Setting up remote desktop and cloud environments
Exploring AutoML Capabilities
- Key features of AutoML frameworks
- Strategies for hyperparameter optimization and search
- Analyzing AutoML outputs and logs
Algorithm Selection in AutoML
- Gradient Boosting Machines (GBMs), Random Forests, GLMs
- Neural networks and deep learning backends
- Balancing accuracy, interpretability, and cost
Data Preparation and Preprocessing
- Handling numeric and categorical data
- Feature engineering and encoding strategies
- Addressing missing values and data imbalance
AutoML for Diverse 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
- Constructing pipelines for real-time prediction
- Monitoring model drift and implementing 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 resolution techniques
- Interpreting AutoML model performance
- Industry case studies
Summary and Next Steps
Requirements
- Familiarity with machine learning algorithms
- Experience in Python or R programming
Target Audience
- Data analysts
- Data scientists
- Data engineers
- Developers
14 Hours
