Course Outline
Introduction to AI Coding Assistants
- Overview of AI in software engineering.
- History and evolution of AI coding assistants.
- Key features and capabilities.
Technologies Behind AI Coding Assistants
- Machine learning and natural language processing.
- Code analysis and generation algorithms.
- Integration with development environments.
Popular AI Coding Assistant Tools
- Comparative study of different tools.
- Hands-on sessions with tools like GitHub Copilot, IntelliCode, etc.
- Community contributions and extensions.
Best Practices and Workflow Integration
- Incorporating AI assistants into daily workflows.
- Collaboration with AI assistants.
- Customizing and training your AI assistant.
Case Studies and Real-World Applications
- Success stories of AI assistants in development projects.
- Limitations and challenges.
- Future trends and developments.
Ethical Considerations and Responsible Use
- Addressing bias and fairness in AI tools.
- Intellectual property and code ownership.
- Privacy and security implications.
Project Work
- Developing a mini project with the aid of an AI coding assistant.
- Peer reviews and feedback sessions.
Summary and Next Steps
Requirements
- A solid understanding of fundamental software development concepts.
- Experience with at least one programming language (e.g., Python, JavaScript).
- Familiarity with integrated development environments (IDEs).
Target Audience
- Software developers.
- Technical team leads.
- Product managers.
Testimonials (2)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
Michal Maj - XL Catlin Services SE (AXA XL)
Course - GitHub Copilot for Developers
Trainer able to adjust the course level during training to fit our understanding level on the topic, so that we could gain more useful knowledge that could further help us harness the tools in our daily works.
