Machine Learning Engineering Specialisation

Course overview

Goals of the Training The focus of the Machine Learning Engineering Specialisation is on imparting knowledge and skills in the field of data science and machine learning. The content includes fundamental and advanced techniques of data analysis, programming with Python, as well as the use of big data technologies. Advanced mathematics and statistics form the basis for understanding complex algorithms in machine learning (ML) and deep learning. Additionally, aspects of software development for data science and data engineering are covered, complemented by practical projects and case studies. IT security and data protection round off the program and ensure a comprehensive understanding of the relevant topics. Contents of the Training Data Science Introduction to Data Science Use cases and performance evaluation Data preprocessing Data processing Selected mathematical techniques Selected artificial intelligence techniques Programming with Python Introduction to the Python programming language Object-oriented concepts in Python Handling exceptions and errors The Python library ecosystem Environments and package management Documentation and testing Version control Big Data Technologies Data types and data sources Databases Modern storage frameworks Data formats Distributed data processing Software Engineering for Data Science Agile project management DevOps Software development API From model to production Advanced Mathematics Calculus Integral transformations Vector algebra Vector calculus Matrices and vector spaces Information theory Advanced Statistics Introduction to statistics Important probability distributions and their applications Bayesian statistics Descriptive statistics Data visualization Parameter estimation Hypothesis testing Machine Learning Supervised, unsupervised, and reinforcement learning approaches Regression and classification of learning problems Estimating functional dependencies using regression methods Data clustering Support vector machines, large margin classification Learning in decision trees Deep Learning Introduction to neural networks and deep learning Network architectures Neural network training Alternative training methods Other network architectures Data Engineering Fundamentals of data engineering Paradigms for data processing at a 1:1 scale Overview of data governance, security, and data protection Common cloud platforms DataOps approach Project: Data Engineering Knowledge transfer and application to practical problems Implementation of a data infrastructure component IT Security and Data Protection Data protection and privacy Components of IT security IT security management Cryptographic concepts Cryptography applications Case Study: Model Engineering Data science methods Data quality Feature engineering Feature selection Building a predictive model Avoiding common pitfalls

Upcoming dates

• 2026-10-22T22:00:00.000Z · 2027-10-22T22:00:00.000Z · Duisburg · Blended Learning
• 2026-10-22T22:00:00.000Z · 2027-10-22T22:00:00.000Z · Karlsruhe, Baden · Blended Learning
• 2026-10-22T22:00:00.000Z · 2027-10-22T22:00:00.000Z · München · Blended Learning
• 2026-10-22T22:00:00.000Z · 2027-10-22T22:00:00.000Z · Mainz am Rhein · Blended Learning
• 2026-10-22T22:00:00.000Z · 2027-10-22T22:00:00.000Z · Köln · Blended Learning