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LightGBM for Machine Learning

 21 Hours
This instructor-led, live training in the US (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.
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XGBoost for Gradient Boosting

 14 Hours
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use XGBoost to build models that efficiently solve regression, classification, ranking, and prediction problems.By the end of this training, participants will be able to:
  • Install and configure XGBoost.
  • Understand the relationship between decision trees and other algorithms such as logistic regression and random forest.
  • Test different libraries to determine the best one for the job.
  • Choose the right configuration for an algorithm.
  • Tune the hyper-parameters of an algorithm for a given dataset.
  • Implement a machine learning solution that balances power with complexity, explainability, and ease of implementation.
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AdaBoost Python for Machine Learning

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at data scientists and software engineers who wish to use AdaBoost to build boosting algorithms for machine learning with Python.By the end of this training, participants will be able to:
  • Set up the necessary development environment to start building machine learning models with AdaBoost.
  • Understand the ensemble learning approach and how to implement adaptive boosting.
  • Learn how to build AdaBoost models to boost machine learning algorithms in Python.
  • Use hyperparameter tuning to increase the accuracy and performance of AdaBoost models.
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Machine Learning with Random Forest

 14 Hours
This instructor-led, live training in the US (online or onsite) is aimed at data scientists and software engineers who wish to use Random Forest to build machine learning algorithms for large datasets.By the end of this training, participants will be able to:
  • Set up the necessary development environment to start building machine learning models with Random forest.
  • Understand the advantages of Random Forest and how to implement it to resolve classification and regression problems.
  • Learn how to handle large datasets and interpret multiple decision trees in Random Forest.
  • Evaluate and optimize machine learning model performance by tuning the hyperparameters.
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