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
 

Testimonials

Related Courses

Data Mining with Weka

  14 hours

AdaBoost Python for Machine Learning

  14 hours

Machine Learning with Random Forest

  14 hours

Machine Learning for Mobile Apps using Google’s ML Kit

  14 hours

DataRobot

  7 hours

Artificial Intelligence (AI) with H2O

  14 hours

H2O AutoML

  14 hours

AutoML with Auto-sklearn

  14 hours

AutoML with Auto-Keras

  14 hours

AutoML

  14 hours

Google Cloud AutoML

  7 hours

RapidMiner for Machine Learning and Predictive Analytics

  14 hours

Pattern Recognition

  21 hours

Pattern Matching

  14 hours

Apache SystemML for Machine Learning

  14 hours