Machine Learning with AI

Course overview

Objective The course "Machine Learning with AI" demonstrates how AI Software Engineers integrate machine learning models into software products and how ML skills are combined with modern AI engineering methods. This training provides practical ML fundamentals from a software development perspective and shows how machine learning and agentic AI interact. The course is aimed at software engineers who want to learn machine learning in a practical way and integrate it into their development work. Content (Selection) Fundamentals of Machine Learning for AI Software Engineers: Concepts and Applications Training and Evaluating ML Models: A Practical Overview LLMs and Classical Machine Learning: Differences and Similarities ML Integration into Software Products: Embedding Models in APIs and Applications Feature Engineering for Machine Learning: Preparing and Transforming Data MLOps: Deploying and Operating Machine Learning Models AutoML and AI-Driven Machine Learning: Faster Path to Better Models Agentic AI and Machine Learning: Combining AI Agents with ML Models Methodology This training is designed as a practical ML engineering seminar, where participants develop machine learning models and integrate them into their own software products. Trainers with ML and software engineering experience accompany the training. Competencies (Outcome) Apply machine learning fundamentals as an AI Software Engineer Integrate and operate ML models in software products Introduce and manage MLOps processes for ML models Productively use AutoML and AI-driven ML tools Combine machine learning and agentic AI in software projects Career Perspectives and Practical Application AI Software Engineers with machine learning skills are in demand throughout the tech industry. Graduates can apply for relevant positions as ML Engineers, AI Engineers, or Senior Developers. Summary This course provides practical machine learning training for AI Software Engineers. From ML fundamentals to MLOps, all relevant technologies are directly integrated into software projects. Tools (Examples) Python, scikit-learn, Hugging Face, FastAPI, GitHub Copilot, Claude, W&B, Docker

Upcoming dates

• 2026-07-28T22:00:00.000Z · 2026-10-20T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-07-28T22:00:00.000Z · 2027-01-12T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-27T23:00:00.000Z · 2027-01-18T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-27T23:00:00.000Z · 2027-04-12T22:00:00.000Z · Virtuelles Klassenzimmer