Online or onsite, instructor-led live Ensemble Learning training courses demonstrate through interactive hands-on practice how to use ensemble machine learning techniques to combine multiple models for improved prediction accuracy and robustness.
Ensemble Learning training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Ensemble Learning trainings in Västmanland can be carried out locally on customer premises or in NobleProg corporate training centers.
Ensemble Learning is also known as Ensemble Methods or Ensemble Modeling.
NobleProg -- Your Local Training Provider
Västerås
NobleProg classroom in Clarion Collection Hotel Etage Västerås, Stora Gatan 32, sweden, 722 12
Located in the heart of Västerås city. From here, the city's shopping is just outside the door. The train station, Västerås harbor and the old Kyrkbacken part of Västerås with its historic buildings and charming alleys are all within walking distance. Västerås Airport is only a 10-minute drive from the hotel.
This instructor-led, live training in Västmanland (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.
This instructor-led, live training in Västmanland (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.
This instructor-led, live training in Västmanland (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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