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Course Outline
Introduction to LightGBM
- What is LightGBM?
- Why choose 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 into machine learning.
- How decision tree algorithms work.
Getting Started with LightGBM
- Setting up the development environment.
- Installing LightGBM as a standalone 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
- Using LightGBM with Python.
- Using LightGBM with R.
- Using LightGBM with SQL.
Deploying LightGBM Models
- Exporting LightGBM models.
- Utilizing LightGBM in production environments.
- Common deployment scenarios.
Troubleshooting LightGBM
- Common issues with LightGBM and their resolutions.
- 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 applying LightGBM in real-world scenarios.
Requirements
- Proficiency in Python programming.
- Prior experience with machine learning.
- Foundational knowledge of decision tree algorithms.
Audience
- Developers
- Data scientists
21 Hours