Reviews 5,0
Reviews 5,0
AI+ Developer Practitioner™
The boundary between traditional software development and artificial intelligence is blurring. This course shows how modern AI tools can be purposefully integrated into development workflows – from the first line of code to the finished, intelligent application.
Key topics
- Automating development workflows with AI support.
- Generating code and tests using prompt engineering.
- Embedding AI models into existing system architectures.
- Improving debugging and quality assurance with AI tools.
- Using generative AI and AI agents as development instruments.
Prerequisite
This course is aimed at people who already have experience in software development and want to use AI capabilities in a productive way.
Target audience
Developers, IT architects, and professionals from technology management – anyone looking to actively integrate AI into their daily work.
The skills gained enable participants to identify AI potential early and embed it purposefully into their own projects and products.
course content
Foundations of Artificial Intelligence
- Types and categories of AI
- Subfields of artificial intelligence
- Practical applications and use cases in business
Mathematical foundations of AI
- Vector operations and linear algebra
- Differential and integral calculus
- Statistics and probability theory
- Fundamentals of discrete mathematics
Python in AI development
- Core Python concepts
- Essential libraries for AI projects
Machine learning in practice
- Fundamentals of machine learning
- Supervised learning methods and algorithms
- Unsupervised learning methods and algorithms
- Approaches to model evaluation and selection
Deep learning and neural networks
- Architecture and function of neural networks
- Optimizing model performance
- Hands-on: Evaluating and improving AI models
Computer vision and image analysis
- Introduction to visual data processing
- Object recognition with neural networks
- Semantic image segmentation
- Generative adversarial networks (GANs)
Natural Language Processing (NLP)
- Text preprocessing and vector representation
- Automated text classification
- Named entity recognition (NER)
- Question answering systems (QA)
Reinforcement learning
- Core principles of reinforcement learning
- Q-learning and Deep Q-Networks (DQNs)
- Policy gradient algorithms
AI in the cloud
- Cloud infrastructure for AI applications
- Machine learning on cloud platforms
Large Language Models (LLMs)
- Architecture and function of LLMs
- Text generation and automated translation
- Knowledge extraction and question answering
Advanced AI research fields
- Neuro-symbolic approaches in AI
- Explainable AI systems (XAI)
- Federated and privacy-preserving learning
- Meta-learning and few-shot methods
Communicating and documenting AI projects
- Communicating AI projects to stakeholders
- Technical documentation of AI systems
- Ethical principles in AI development
Optional module: AI agents in practice
- Principles and functionality of AI agents
- Application examples and case studies
- Hands-on exercises with 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.
