AutoML Training and Certification in Thimphu, Bhutan

This AutoML training provides a comprehensive understanding of Automated Machine Learning (AutoML) and its applications. Participants will learn how to use AutoML tools to automate feature engineering, model selection, and hyperparameter tuning to build efficient machine learning models without extensive manual effort.

Introduction to AutoML & Automated Model Building

• Module 1: Understanding AutoML & Its Use Cases
  • What is AutoML? Benefits and limitations
  • Traditional ML vs. AutoML
  • Popular AutoML tools (Google AutoML, H2O.ai, Auto-sklearn, TPOT)
• Module 2: Data Preprocessing & Feature Engineering with AutoML
  • Automated data cleaning, transformation, and augmentation
  • Feature extraction and selection using AutoML
  • Handling missing values and categorical data
• Module 3: Automated Model Selection & Hyperparameter Tuning
  • How AutoML selects models (Decision Trees, Random Forest, Neural Networks, etc.)
  • Hyperparameter tuning with AutoML
  • Performance evaluation and metric selection

AutoML Tools, Deployment & Real-World Use Cases

• Module 4: Working with Leading AutoML Frameworks
  • Google AutoML: Cloud-based ML automation
  • H2O.ai: Open-source AutoML
  • TPOT & Auto-sklearn: AutoML in Python
• Module 5: Deploying AutoML Models & Interpretation
  • Exporting and deploying models in production environments
  • Model explainability & bias detection
  • Monitoring and retraining AutoML models
• Module 6: Real-World Applications & Case Studies
  • AutoML in healthcare, finance, and e-commerce
  • Future trends in AutoML and its impact on ML practitioners
• Hands-On Labs