Ensemble Learning Training in Pierre

This instructor-led, live training in Pierre (online or onsite) is designed for government developers and data scientists at the beginner to intermediate level who wish to learn the fundamentals of LightGBM and explore advanced techniques.

By the end of this training, participants will be able to:

• Install and configure LightGBM for government use.
• Understand the theoretical foundations of gradient boosting and decision tree algorithms.
• Utilize LightGBM for both basic and advanced machine learning tasks.
• Implement advanced techniques, including feature engineering, hyperparameter tuning, and model interpretation.
• Integrate LightGBM with other machine learning frameworks used in government applications.
• Address common issues that arise when using LightGBM for government projects.

This instructor-led, live training in Pierre (online or onsite) is designed for data scientists and software engineers who wish to utilize AdaBoost to develop boosting algorithms for machine learning with Python.

By the end of this training, participants will be able to:

• Establish the required development environment to begin building machine learning models with AdaBoost.
• Gain a comprehensive understanding of the ensemble learning methodology and how to implement adaptive boosting techniques.
• Acquire the skills to construct AdaBoost models to enhance machine learning algorithms in Python.
• Apply hyperparameter tuning to optimize the accuracy and performance of AdaBoost models for government applications.

This instructor-led, live training in Pierre (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to develop machine learning algorithms for large datasets.

By the end of this training, participants will be able to:

• Configure the required development environment to initiate building machine learning models with Random Forest.
• Comprehend the benefits of Random Forest and its application in addressing classification and regression challenges.
• Manage large datasets and interpret multiple decision trees within the Random Forest framework.
• Assess and enhance machine learning model performance through hyperparameter tuning, ensuring optimal results for government applications.