Technologies / Artificial Intelligence

LightGBM in machine learning

The training deepens the knowledge of using the LightGBM framework in advanced machine learning projects. Participants will learn techniques for optimizing and tuning gradient boosting models. The program combines theory with practical workshops, allowing participants to understand the mechanisms of the algorithm and use it effectively in real-world applications.

Issues

LightGBM architecture
Optimization of hyperparameters
Regularization techniques
Distributed learning
Custom target functions
Categorical data handling
Early stopping
Serialization of models
Performance monitoring
Learning strategies
Debugging models
Production updates

Benefits

The participant will develop the ability to effectively use LightGBM in ML projects
Will learn to optimize model performance through advanced parameter tuning techniques
Will learn methods to effectively handle large data sets in the learning process
Will gain knowledge of implementing custom objective functions and metrics
Will be able to implement LightGBM models in a production environment
Will gain the ability to monitor and maintain models in real-time
Will develop the ability to debug and troubleshoot models
Will learn to select optimal learning strategies for different use cases

Who is this training for?

Data Scientists working with gradient boosting models

ML engineers optimizing model performance

Data analysts in predictive projects

Machine learning specialists

ML systems programmers

Researchers involved in predictive modeling

Data analysis experts

Algorithm optimization specialists

Prerequisites

Practical knowledge of machine learning
Experience in working with tree models
Familiarity with Python and data analysis libraries
Fundamentals of numerical optimization

Training program

Comparison with other solutions
Configuring the environment

Optimization of models
Selection of hyperparameters
Regularization techniques
Categorical data handling

Advanced functionalities

Handling large data sets
Custom target functions

Implementation and monitoring
Serialization of models
Integration with production systems
Performance monitoring
Model updates

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Frequently asked questions

What are the prerequisites for this training?

For LightGBM in machine learning we recommend: Practical knowledge of machine learning; Experience in working with tree models; Familiarity with Python and data analysis libraries.

What is the format and duration of this training?

The training lasts 3 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Data Scientists working with gradient boosting models; ML engineers optimizing model performance; Data analysts in predictive projects.