Machine Learning

Course overview

The training course Fundamentals of Machine Learning provides a clear and practical foundation in the methods and application areas of Machine Learning. Participants learn how to use data to train models, recognize patterns, and make predictions. The course is aimed at beginners who want a systematic approach to the fundamentals of Machine Learning. !Introduction to the Fundamentals of Machine Learning *Importance of the fundamentals of Machine Learning in business and society *Distinction between Machine Learning, AI, and classical programming *Typical application areas: forecasts, classification, recommendation systems *Opportunities, limitations, and ethical questions !Data as the Basis of Machine Learning *Role of data quality and data preparation in Machine Learning *Training, validation, and test data *Feature Engineering: selection and creation of relevant features *Dealing with biases and data errors !Methods and Models in Machine Learning *Supervised and unsupervised Machine Learning *Basic model types: regression, classification, clustering *Model training, validation, and evaluation *Understanding overfitting and generalization !Tools and Practical Application *Typical tools and platforms for Machine Learning (e.g., Python, Notebooks, AutoML) *Classification of libraries such as scikit-learn or TensorFlow *Example application projects from practical fields *Data protection and responsible use of Machine Learning Upon completion of the training, participants will understand the key concepts of the fundamentals of Machine Learning. They will be able to classify basic methods, assess data requirements, and conceptually design simple Machine Learning projects. This lays a solid foundation for further deepening in Data Science, AI applications, and analytical modeling.

Upcoming dates

• 2026-10-04T22:00:00.000Z · 2027-01-09T23:00:00.000Z · München · Hybrid Learning
• 2026-10-04T22:00:00.000Z · 2027-01-09T23:00:00.000Z · Aachen · Hybrid Learning
• 2026-10-04T22:00:00.000Z · 2027-01-09T23:00:00.000Z · Berlin · Hybrid Learning
• 2026-10-04T22:00:00.000Z · 2027-01-09T23:00:00.000Z · Berlin · Hybrid Learning
• 2026-10-04T22:00:00.000Z · 2027-01-09T23:00:00.000Z · Berlin · Hybrid Learning