AutoML Essentials Training Course

Introduction and Environment Setup

• Understanding AutoML and its significance
• Setting up Python and R environments
• Configuring remote desktop and cloud infrastructures

Exploring AutoML Capabilities

• Fundamental features of AutoML frameworks
• Hyperparameter optimization and search methods
• 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

• Managing numeric and categorical data
• Feature engineering and encoding techniques
• 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
• Creating pipelines for real-time predictions
• Monitoring model drift and implementing retraining strategies

Ensembling and Advanced Topics

• Combining AutoML models through stacking and blending
• Privacy and regulatory compliance aspects
• Cost optimization for large-scale AutoML deployments

Troubleshooting and Case Studies

• Common errors and their solutions
• Evaluating AutoML model performance
• Industry case studies

Summary and Next Steps