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Duration 14 hours
Course Outline
Introduction to AI-Powered Coding Assistance Tools
- Overview of AI applications within software engineering
- Historical context and developmental trajectory of AI coding assistants
- Core features and functional capabilities
Underlying Technologies for AI Coding Assistants
- Applications of machine learning and natural language processing
- Algorithms for code analysis and generation
- Integration protocols with development environments
Widely Adopted AI Coding Assistant Platforms
- Comparative analysis of various AI tools
- Practical sessions utilizing platforms such as GitHub Copilot and IntelliCode
- Community-driven contributions and extension mechanisms
Best Practices and Workflow Integration Strategies
- Incorporating AI assistants into standard operational workflows
- Effective collaboration methodologies with AI systems
- Configuration and training of AI assistants for government contexts
Case Studies and Practical Applications
- Evaluation of limitations and operational challenges
- Emerging trends and future technological developments
Ethical Implications and Responsible Deployment
- Mitigation of bias and ensuring fairness in AI tools
- Intellectual property rights and code ownership issues
- Privacy safeguards and security implications
Applied Project Work
- Development of a pilot project utilizing an AI coding assistant
- Peer review processes and structured feedback sessions
Summary and Recommended Next Steps
Requirements
- Proficiency in at least one programming language (e.g., Python, JavaScript)
- Familiarity with integrated development environments (IDEs)
Target Audience
- Product Managers
Testimonials (1)
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
