Machine Learning & Statistics

Course overview

Upon completion of the training, you will have knowledge of machine learning methods and basic statistical techniques. You will also be able to train models and perform statistical analyses. Terminology and notation in machine learning and statistics Data preparation: handling missing values, one-hot encoding, and standardization Algorithm classes: Perceptron, k-Nearest Neighbors, Random Forests, decision trees, and ensemble methods Model evaluation and hyperparameter tuning including accuracy measurement and confusion matrix Basics of probability theory, distributions, as well as linear regression and confidence intervals

Upcoming dates

• 2026-09-13T22:00:00.000Z · 2026-11-01T23:00:00.000Z · Augsburg, Bayern · Combined Learning
• 2026-09-13T22:00:00.000Z · 2026-11-01T23:00:00.000Z · München · Combined Learning
• 2026-09-13T22:00:00.000Z · 2026-11-01T23:00:00.000Z · Hamburg · Combined Learning
• 2026-09-13T22:00:00.000Z · 2026-11-01T23:00:00.000Z · Düsseldorf · Combined Learning
• 2026-09-13T22:00:00.000Z · 2026-11-01T23:00:00.000Z · Essen, Ruhr · Combined Learning