Programming Machine Learning Models

Course overview

The training program on Programming Machine Learning Models imparts technical and analytical skills for developing learning models for data-driven applications. The content is structured around data preparation, model programming, and performance evaluation. Data Basis and Preparation Classify data sources for modeling tasks Clean, transform, and split datasets Define features and target variables for models Prepare training, testing, and validation data Model Programming Programmatically apply machine learning algorithms Develop models for classification and regression Control parameters and training processes in code Utilize libraries for modeling and evaluation Training and Optimization Train models and compare results Adjust hyperparameters and improve model performance Recognize overfitting and implement countermeasures Analyze sources of error in data and training processes Evaluation and Deployment Evaluate model results using appropriate metrics Professionally review and interpret predictions Document model code and structure it for reproducibility Prepare models for further application steps The training enables programming, optimization, and evaluation of machine learning models for data-driven tasks.

Upcoming dates

• 2026-08-16T22:00:00.000Z · 2026-11-07T23:00:00.000Z · Berlin · Hybrid Learning
• 2026-08-16T22:00:00.000Z · 2027-01-30T23:00:00.000Z · Berlin · Hybrid Learning
• 2026-08-16T22:00:00.000Z · 2026-11-07T23:00:00.000Z · Bielefeld · Hybrid Learning
• 2026-08-16T22:00:00.000Z · 2027-01-30T23:00:00.000Z · Bielefeld · Hybrid Learning
• 2026-08-16T22:00:00.000Z · 2026-11-07T23:00:00.000Z · Bonn · Hybrid Learning