H2O.ai Training and Certification in Kuala Lumpur, Malaysia

This course provides a comprehensive introduction to H2O.ai, an open-source, scalable, and distributed machine learning platform. Participants will explore H2O-3, H2O AutoML, and Driverless AI, learning how to build, tune, and deploy machine learning models efficiently. The course covers data preprocessing, feature engineering, model interpretability, and real-world deployment strategies using H2O.ai tools. Hands-on exercises will reinforce theoretical concepts, enabling participants to apply H2O.ai solutions to various AI and ML use cases.

Introduction to H2O.ai and H2O-3

• Overview of H2O.ai and its ecosystem
• Installing and setting up H2O.ai in Python/R
• Understanding H2O-3 architecture and distributed computing
• Loading and preprocessing datasets in H2O-3
• Hands-on: Building basic machine learning models with H2O-3

AutoML and Model Optimization in H2O.ai

• Introduction to H2O AutoML and its capabilities
• Running automated machine learning experiments
• Comparing models and selecting the best-performing one
• Understanding feature importance and model interpretability
• Hands-on: Using H2O AutoML for regression and classification

Advanced H2O.ai – Driverless AI and Model Deployment

• Introduction to H2O Driverless AI and its AutoML capabilities
• Automated feature engineering and hyperparameter tuning
• Explainability and bias detection in AI models
• Deploying H2O.ai models using REST APIs and cloud platforms
• Hands-on: End-to-end AI pipeline with H2O Driverless AI