- Presencial
- 21 horas
- Aula Virtual
- No disponible
LightGBM is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification, and other machine learning tasks.
This instructor-led, live training (online or onsite) is aimed at beginner to intermediate-level developers and data scientists who wish to learn the basics of LightGBM and explore advanced techniques.
By the end of this training, participants will be able to:
- Install and configure LightGBM.
- Understand the theory behind gradient boosting and decision tree algorithms
- Use LightGBM for basic and advanced machine learning tasks.
- Implement advanced techniques such as feature engineering, hyperparameter tuning, and model interpretation.
- Integrate LightGBM with other machine learning frameworks.
- Troubleshoot common issues in LightGBM.
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
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