Machine Learning

Course overview

Learning patterns from data involves a variety of steps in practice, from feature engineering to model evaluation. In the machine learning program, supervised and unsupervised models are trained. The methods and evaluation follow a fixed procedure. !Course Content !1. Learning Tasks and Data Foundation *Distinction between regression, classification, and clustering *Requirements for data volume and data quality *Division into training and test sets !2. Feature Engineering and Preprocessing *Creation and selection of features *Scaling and encoding of feature values *Handling missing and distorted values !3. Training and Parameter Selection in Machine Learning *Training and comparing supervised methods *Clustering methods without a target variable *Adjustment of method parameters !4. Evaluation and Limitations of Models *Metrics for quality and types of errors *Recognizing overfitting *Naming the limitations of a model's statements !Professional Relevance *Machine learning as the foundation of data-driven applications *Robust evaluation of models before deployment *Connection between data work and model development

Upcoming dates

• 2026-10-25T23:00:00.000Z · 2027-01-22T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-25T23:00:00.000Z · 2027-04-22T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-25T23:00:00.000Z · 2027-01-22T23:00:00.000Z · Berlin · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-04-22T22:00:00.000Z · Berlin · Combined Learning