Machine Learning

Course overview

Machine Learning is the methodological foundation of modern AI applications - it enables computer systems to learn from data and recognize patterns without being explicitly programmed. A solid understanding of ML is a prerequisite for professionals in Data Science and IT. Course Content Fundamentals of Machine Learning Learning paradigms: supervised, unsupervised, reinforcement Training data, features, and labels Bias-variance tradeoff and generalization Supervised Learning Methods Linear and logistic regression Decision trees and ensemble methods Support Vector Machines and k-Nearest Neighbor Unsupervised Learning Methods Clustering: k-Means and hierarchical clustering Dimensionality reduction: PCA and t-SNE Anomaly detection Model Training and Evaluation Training, validation, and test sets Metrics: Accuracy, Precision, Recall, F1, AUC Cross-validation and hyperparameter tuning Professional Relevance Relevant for Data Scientists and ML professionals Foundation for the development and evaluation of ML models Preparation for specialized ML qualifications

Upcoming dates

• 2026-10-25T23:00:00.000Z · 2027-01-30T23:00:00.000Z · Berlin · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-05-08T22:00:00.000Z · Berlin · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-01-30T23:00:00.000Z · Augsburg, Bayern · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-05-08T22:00:00.000Z · Augsburg, Bayern · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-01-30T23:00:00.000Z · Bielefeld · Combined Learning