Machine Learning Engineer

Course overview

The Machine Learning Engineer develops learning models and integrates them into data-driven applications: from extracting data from SQL databases to model training and accuracy evaluation in Python, to presenting model results in Power BI. Machine Learning Engineering is treated in this course as a continuous chain of algorithm selection, data preparation, training, and model application. Competency Goals Differentiate types of machine learning algorithms and their areas of application Extract data from SQL databases and prepare it for model training Train models using Python and evaluate accuracy with scikit-learn, pandas, and NumPy Transfer forecasting and classification models to real-world scenarios Assess model quality in data-driven projects Present model results in Power BI reports and dashboards Topics and Connections The foundation consists of the central methods and terms of machine learning as well as the role of artificial intelligence in data analysis. At the core of Machine Learning Engineering is data preparation: extraction from SQL databases, cleaning, and preparation for training. Models are trained using Python, scikit-learn, pandas, and NumPy, their accuracy measured, and methods compared. The model application transfers trained models to data-driven scenarios such as forecasting and data classification, with reference to business intelligence. Power BI is used to design reports and dashboards that make model results usable for data-based decisions. In the final project, a machine learning project is independently implemented in the role of the ML Engineer, merging model development with data evaluation. Career Prospects Fields of application arise in development and AI departments that develop models for forecasting and classification and integrate them into business processes. Machine Learning Engineer or ML Engineer for model training and application AI Engineer with a focus on classical machine learning Data Scientist focusing on model development and evaluation Software Developer for machine learning components

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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