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Course Outline
Introduction to AI Coding Assistants
- A look at the role of AI in software engineering
- The historical context and evolution of AI coding tools
- Core features and key capabilities
Underlying Technologies of AI Coding Assistants
- Machine learning and natural language processing fundamentals
- Algorithms for code analysis and generation
- Seamless integration with development environments
Leading AI Coding Assistant Tools
- A comparative analysis of various tools available in the market
- Practical sessions using tools such as GitHub Copilot and IntelliCode
- Leveraging community contributions and available extensions
Best Practices and Workflow Integration
- Embedding AI assistants into everyday development workflows
- Effective collaboration strategies with AI tools
- Methods for customizing and training your specific AI assistant
Case Studies and Real-World Scenarios
- Success stories featuring AI assistants in live development projects
- Identifying limitations and potential challenges
- Emerging trends and future developments
Ethics and Responsible Usage
- Mitigating bias and ensuring fairness in AI tools
- Navigating intellectual property rights and code ownership
- Understanding privacy and security implications
Practical Project Work
- Creating a mini-project guided by an AI coding assistant
- Conducting peer reviews and structured feedback sessions
Wrap-up and Future Directions
Requirements
- A solid grasp of fundamental software development principles
- Practical experience with at least one programming language (such as Python or JavaScript)
- Competence in using integrated development environments (IDEs)
Target Audience
- Software developers
- Technical leads and managers
- Product managers
14 Hours
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