LightGBM for Machine Learning

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

Audience

• Developers
• Data scientists

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.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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