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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
Requirements
- An understanding of Python programming
- Experience with machine learning
- Basic knowledge of decision tree algorithms
Audience
- Developers
- Data scientists
21 Hours
