{"id":89065,"date":"2025-05-27T17:24:27","date_gmt":"2025-05-27T15:24:27","guid":{"rendered":"https:\/\/www.sixsigmaclub.de\/event\/workshop-statistical-modeling-machine-learning\/"},"modified":"2025-05-27T17:24:27","modified_gmt":"2025-05-27T15:24:27","slug":"workshop-statistical-modeling-machine-learning","status":"publish","type":"tribe_events","link":"https:\/\/www.sixsigmaclub.de\/en\/event\/workshop-statistical-modeling-machine-learning\/","title":{"rendered":"Workshop: Statistical Modeling & Machine Learning"},"content":{"rendered":"

Participants acquire in-depth knowledge of modern data modeling, are able to integrate ML methods into their work in a meaningful way and support data-driven decisions for process improvements.<\/p>\n

Core content of this module:<\/strong><\/p>\n

\u27a1 Multivariate methods and classical regression models (refresher) to refresh statistical basics.
\n\u27a1 Introduction to data orchestration and machine learning – understanding supervised and unsupervised learning.
\n\u27a1 Model setup with KNIME\u00ae, Minitab\u00ae and Python, including data preparation, feature engineering and validation.
\n\u27a1 Classification and regression methods such as decision trees, Naive Bayes or neural networks.
\n\u27a1 Differences between classical and ML-based models, including fields of application and limitations.
\n\u27a1 Practical application of ML methods for process optimization, including visualization and interpretation.
\n\u27a1 Confident handling of model quality, cross-validation and model comparison for well-founded decisions.<\/p>\n

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Dates (ONLINE)<\/strong><\/h3>\n