Machine Learning: Fundamentals & Practice

Course overview

Upon completion of the training, you will have fundamental knowledge in Machine Learning and practical experience in applying various algorithms. You will also be able to prepare datasets and train and evaluate models using Scikit-Learn. Terminology and notation in Machine Learning Installation and use of Python packages for Machine Learning Preparation and transformation of data including One-Hot Encoding and standardization Training, hyperparameter tuning, and model evaluation with k-Means, Random Forests, and K-Nearest Neighbors Development and presentation of a Machine Learning project to analyze model accuracy

Upcoming dates

• 2026-10-18T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Köln · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Leipzig · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Stuttgart · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Gütersloh · Combined Learning
• 2026-10-18T22:00:00.000Z · 2026-11-15T23:00:00.000Z · Greifswald, Hansestadt · Combined Learning