Python and Machine Learning

Course overview

The fundamentals of Machine Learning with Python and scikit-learn are at the center of this course: concepts of machine learning are directly translated into code, models are developed, validated, and evaluated. Supervised and unsupervised learning form the conceptual framework, while Python programming serves as the tool. Competency Goals Differentiate between supervised and unsupervised learning Prepare training and test data for model building Train models with scikit-learn and validate them using cross-validation Use quality measures such as accuracy and error rate for model evaluation Connect Python programming and data analytics to make precise predictions Present results of trained models using Python visuals or Power BI Topics and Connections The Machine Learning course first organizes the basic concepts: features and target variables, training and testing, underfitting and overfitting. With Python and scikit-learn, this results in a repeatable process of data preparation, model development, and model validation. Model evaluation methods demonstrate how reliably a model performs on unseen data. Programming and data analytics interconnect when trained models provide predictions and their results are evaluated. In the project, a custom machine learning model is developed, tested with real data, and presented in Power BI or with Python visuals. The fundamentals of machine learning remain focused on simple, comprehensible models that Python programming makes directly usable in data analysis. Career Perspectives Application fields can be found in departments that use Python in data analysis and develop initial models for analytical questions. Junior Machine Learning Developer for simple models Python Developer with a focus on data analysis Data Analyst with predictive models in scikit-learn Business Analytics Specialist for interpreting model results

Upcoming dates

• 2026-11-22T23:00:00.000Z · 2027-01-02T23:00:00.000Z · Heilbronn, Neckar · Hybrid Learning
• 2026-11-22T23:00:00.000Z · 2027-01-02T23:00:00.000Z · Leinfelden-Echterdingen · Hybrid Learning
• 2026-11-22T23:00:00.000Z · 2027-01-02T23:00:00.000Z · Nürnberg, Mittelfranken · Hybrid Learning
• 2026-11-22T23:00:00.000Z · 2027-01-02T23:00:00.000Z · Karlsruhe, Baden · Hybrid Learning
• 2026-11-22T23:00:00.000Z · 2027-01-02T23:00:00.000Z · Virtuelles Klassenzimmer

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