Machine Learning

Course overview

Machine learning identifies patterns in data that are difficult to uncover manually. The Machine Learning course covers everything from data preparation to supervised and unsupervised learning, culminating in neural networks. The practical project focuses on how to train models and evaluate their quality. Basics and Data Preparation First, the foundation for every model is established. Introduction to machine learning Data preparation and feature engineering Learning Methods and Evaluation This section focuses on the methods and their assessment. Supervised and unsupervised learning Model training and evaluation Neural Networks and Practice Finally, deeper models and their implementation are covered. Introduction to neural networks Practical project for model development The course combines the methodology of machine learning with practical model work. This allows for the design of individual data projects and the reliable interpretation of results. The content is applied using Python, Jupyter Notebooks, pandas, scikit-learn, TensorFlow, Keras, matplotlib, and seaborn.

Upcoming dates

• 2026-09-13T22:00:00.000Z · 2026-10-11T22:00:00.000Z · Recklinghausen, Westfalen · Combined Learning
• 2026-09-13T22:00:00.000Z · 2026-11-08T23:00:00.000Z · Recklinghausen, Westfalen · Combined Learning
• 2026-09-13T22:00:00.000Z · 2026-10-11T22:00:00.000Z · Eberswalde · Combined Learning
• 2026-09-13T22:00:00.000Z · 2026-10-11T22:00:00.000Z · Heilbronn, Neckar · Combined Learning
• 2026-09-13T22:00:00.000Z · 2026-11-08T23:00:00.000Z · Eberswalde · Combined Learning