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This machine learning training focused training provides a structured introduction to utilizing the tidymodels framework in R for different immunology applications. The course guides participants through a practical, hands-on workflow that covers data preprocessing, feature engineering, and the implementation of advanced algorithms such as XGBoost to uncover significant biological signatures within high-dimensional datasets.
By the end of the training, you will have developed a reproducible and statistically sound pipeline for building, tuning, and evaluating machine learning models, ensuring that your identified immune signatures are both predictive and biologically relevant to your research objectives.
Learning outcomes: Machine learning | Tidymodels R package | Predictive Modelling
Prerequisites: Basic knowledge of R programming
This machine learning training focused training provides a structured introduction to utilizing the tidymodels framework in R for different immunology applications. The course guides participants through a practical, hands-on workflow that covers data preprocessing, feature engineering, and the implementation of advanced algorithms such as XGBoost to uncover significant biological signatures within high-dimensional datasets.
By the end of the training, you will have developed a reproducible and statistically sound pipeline for building, tuning, and evaluating machine learning models, ensuring that your identified immune signatures are both predictive and biologically relevant to your research objectives.
Learning outcomes: Machine learning | Tidymodels R package | Predictive Modelling
Prerequisites: Basic knowledge of R programming