Online Ensemble Learning Training in US

This instructor-led, live training (online or onsite) is designed for government developers and data scientists at beginner to intermediate levels 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. This training is tailored to enhance the technical capabilities of participants, ensuring they are well-equipped to apply these skills effectively in their roles for government.

This instructor-led, live training (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: - Set up the required development environment to begin building machine learning models with AdaBoost. - Understand the ensemble learning methodology and how to implement adaptive boosting techniques. - Learn how to construct AdaBoost models to enhance machine learning algorithms in Python. - Utilize hyperparameter tuning to improve the accuracy and performance of AdaBoost models, ensuring they meet the standards required for government applications.

This instructor-led, live training (online or onsite) is designed for government data scientists and software engineers who wish to utilize Random Forest to develop machine learning algorithms for large datasets.

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

• Set up the required development environment to begin building machine learning models with Random Forest.
• Comprehend the benefits of Random Forest and how to apply it to address classification and regression challenges.
• Learn techniques for managing large datasets and interpreting multiple decision trees within Random Forest.
• Evaluate and enhance machine learning model performance through hyperparameter tuning.