The course “Developing Machine Learning Models” offers a sound and practical introduction to the world of machine learning and data processing using the Python programming language. Theoretical concepts are combined with practical, hands-on exercises to allow participants to apply their knowledge immediately. They are guided step-by-step from fundamental approaches to increasingly complex, advanced methods. The course begins with systems such as supervised learning, unsupervised learning, and reinforcement learning, followed by a more detailed examination of data processing and data analysis. The course concludes with an intensive focus on neural networks and deep learning models.

Particular emphasis is placed on practical application, enabling participants to learn how to develop, train, and evaluate models in real-world projects. They acquire in-depth knowledge of how to optimize and validate machine learning models for application in various use cases. The course is designed for participants who already have basic knowledge of Python and are familiar with mathematical foundations such as linear algebra and statistics. The combination of theory and practice prepares participants for the professional use of machine learning models that address real-world challenges.

• Introduction and Fundamentals of Machine Learning
• Supervised Learning
  • Regression
  • Classification
• Unsupervised Learning
  • Clustering
  • Dimensionality Reduction
• Reinforcement Learning
• Data processing and data analysis
• Model evaluation, validation and optimization
• Neural networks and deep learning