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100% fundable

AI+ Engineer Practitioner™

Those who don’t just understand AI but can actually apply it change how technology is built today. This course delivers exactly that: deep expertise, direct practice and a clear focus on modern AI engineering.

Key topics

  • Building AI-driven software solutions
  • Designing and automating ML pipelines
  • Scalable system architecture for AI models
  • Testing and validating AI models
  • Current MLOps practices and deployment strategies

Prerequisites
A solid foundation in Python and an interest in automation are enough to get started – no prior knowledge of AI is required.

Target group
Developers and IT professionals who want to expand their work with AI capabilities and deliver hands-on projects.

Completing this course means more than knowing the theory – you’ll have learned to build, test and deploy AI systems yourself.
 

course content
  • What AI is and how it has evolved
  • Key techniques and principles of AI
  • Ethical aspects of AI use
  • AI application domains and what makes each unique
  • Structure and components of AI architectures
  • Phases in the AI development lifecycle
  • Exercise: Setting up a simple AI development environment
  • The role and function of activation functions
  • Learning mechanisms: backpropagation and optimization
  • Exercise: Building a neural network with a deep learning framework
  • Neural networks for computer vision and image analysis
  • Sequence models for temporal and language data
  • Implementing neural networks in real-world projects
  • How LLMs work and why they matter
  • Popular language models and their use cases
  • Fine-tuning: adapting language models for specific tasks
  • Sample project: fine-tuning for text classification
  • Structure and principles of GANs (Generative Adversarial Networks)
  • Variational Autoencoders (VAEs) and their use cases
  • Generating synthetic data with generative models
  • Exercise: implementing generative models for image generation
  • NLP methods and their real-world application areas
  • Transformer architectures and attention mechanisms
  • BERT and its strengths for NLP tasks
  • Exercise: building NLP pipelines with pre-trained models
  • What transfer learning is and when to use it
  • Techniques and strategies for effective transfer learning
  • Exercise: adapting Hugging Face models for different tasks
  • Types and use cases of GUIs in AI projects
  • Web-based frameworks for AI interfaces
  • Desktop frameworks for AI applications
  • Presenting AI insights clearly for non-technical audiences
  • Planning and implementing deployment pipelines for AI models
  • Developing prototypes based on concrete requirements
  • Exercise: running through the deployment process
  • How AI agents are built and how they work
  • Case studies from the engineering domain
  • Exercises: developing and testing AI agents

Up to 100% funding for your continuing education

Attractive funding opportunities are available for numerous continuing education programs – depending on the funding program, up to 100% funding is possible.
The requirement is that you live or work in Germany. Our funding opportunities are not available outside of Germany.

Funding opportunities are available for career starters, job seekers, career changers, startups, experienced professionals, those seeking career advancement, and specialists.