Course Outline

Introduction to LightGBM

  • What is LightGBM?
  • Why use LightGBM?
  • Comparison with other machine learning frameworks
  • Overview of LightGBM features and architecture

Understanding Decision Tree Algorithms

  • The lifecycle of a decision tree algorithm
  • How decision tree algorithms fit in with machine learning
  • How decision tree algorithms work

Getting Started with LightGBM

  • Setting up the Development Environment
  • Installing LightGBM as a stand-alone application
  • Installing LightGBM as a container (Docker, Podman, etc.)
  • Installing LightGBM on-premise
  • Installing LightGBM in the cloud (private, AWS, etc.)
  • Basic usage of LightGBM for classification and regression

Advanced Techniques in LightGBM

  • Feature Engineering with LightGBM
  • Hyperparameter Tuning with LightGBM
  • Model Interpretation with LightGBM

Integrating LightGBM with Other Technologies

  • LightGBM with Python
  • LightGBM with R
  • LightGBM with SQL

Deploying LightGBM Models

  • Exporting LightGBM models
  • Using LightGBM in production environments
  • Common deployment scenarios

Troubleshooting LightGBM

  • Common issues with LightGBM and how to resolve them
  • Debugging LightGBM models
  • Monitoring LightGBM models in production

Summary and Next Steps

  • Review of LightGBM basics and advanced techniques
  • Q&A session
  • Next steps for using LightGBM in real-world scenarios


  • An understanding of Python programming
  • Experience with machine learning
  • Basic knowledge of decision tree algorithms


  • Developers
  • Data scientists
  21 Hours


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