AI-Machine-Learning Specialist for AI Applications

Course overview

AI-Machine-Learning specialists understand how learning algorithms work and support their selection, evaluation, and implementation in companies. The training provides application-oriented machine learning knowledge: classifying methods, assessing results, and preparing deployment decisions based on solid foundations - without conducting model training at a development level. Fundamentals of Learning Algorithms To assess ML results, one must understand the principles behind them. - Distinguish between supervised, unsupervised, and reinforcement learning - Classification, regression, and clustering using practical examples - Neural networks and the path to large language models - Generative AI as a special case: possibilities and peculiarities Data as the Foundation of Every Model - Understand the requirements for training and test data - Recognize biases in data and assess their consequences - Data preparation and feature selection in principle Evaluating Models Instead of Believing Them Metrics make model quality debatable. - Interpret quality measures such as accuracy, precision, and types of errors - Recognize overfitting: when models memorize instead of generalizing - Test setup: clean separation of training and evaluation - Contextualize results understandably for specialist departments Supporting Implementation in Companies - Check application cases for ML suitability: data situation, benefits, risks - Compare and select ready-made ML services and models - Structure collaboration with developers and data professionals - Keep operations in mind: model aging and regular re-evaluation Responsibilities and Limitations - Traceability and explainability of model decisions - Assess fairness, bias, and discrimination risks - Legal framework from GDPR and EU AI Act in ML deployment From Method to Application Case The bridge between method and benefit is the core competency of the role. - Typical ML application cases: forecasting, classification, anomaly detection - Structure requirement discussions with specialist departments - Justify feasibility assessments in writing and in a traceable manner - Define pilot phases: scope, success criteria, exit points - Formulate result reports for decision-makers without jargon - Obtain second opinions: organize external evaluations of critical models - Documentation standards for models: purpose, data, limitations, responsible parties This training shows how machine learning methods can be evaluated thoroughly and their implementation in companies can be competently supported.

Upcoming dates

• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Frankfurt am Main · Hybrid Learning
• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Herzogenrath · Hybrid Learning
• 2026-10-20T22:00:00.000Z · 2026-12-22T23:00:00.000Z · Leinfelden-Echterdingen · Hybrid Learning
• 2026-11-03T23:00:00.000Z · 2027-07-03T22:00:00.000Z · Virtuelles Klassenzimmer