This course explores the use of AI in the context of GitHub Copilot, a generative AI tool for developers. It equips users with the knowledge and skills to use Copilot effectively while mitigating potential ethical and operational risks associated with AI usage.
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Course Outline
1. Responsible AI with GitHub Copilot
• Mitigate AI risks
• Microsoft and GitHub’s six principles of responsible AI
2. Introduction to GitHub Copilot
• GitHub Copilot, your AI pair programmer
• Interact with Copilot
• Set up, configure, and troubleshoot GitHub Copilot
3. Introduction to prompt engineering with GitHub Copilot
• Prompt engineering foundations and best practices
• GitHub Copilot user prompt process flow
• GitHub Copilot data
• GitHub Copilot Large Language Models (LLMs)
4. Introduction to Copilot Spaces
• Creating your first space
• Sharing, Discoverability, and Governance
• Do’s and Don’ts of Working in a Space
5. Using advanced GitHub Copilot features
• Advanced GitHub Copilot features
• Applied GitHub Copilot techniques
6. GitHub Copilot Across Environments: IDE, Chat, GitHub.com, Command Line Techniques, and the GitHub Copilot App
• Code completion with GitHub Copilot
• GitHub Copilot Chat
• GitHub Copilot on GitHub.com
• GitHub Copilot for the Command Line
• The GitHub Copilot app
7. Management and customization considerations with GitHub Copilot
• Explore GitHub Copilot plans and their associated management and customization features
• Explore contractual protections in GitHub Copilot and disabling matching public code
• Manage content exclusions
• Troubleshoot common problems with GitHub Copilot
8. Developer use cases for AI with GitHub Copilot
• Boost developer productivity with AI
• Align with developer preferences
• AI in the Software Development Lifecycle (SDLC)
• Understand limitations and measure impact
9. Develop unit tests using GitHub Copilot tools
• Examine Visual Studio Code support for unit testing
• Generate unit tests with the GitHub Copilot Chat view
• Plan and automate test workflows using the Plan and Agent modes
• Extend tests with ghost text and fix failing tests
10. Building applications with GitHub Copilot agent mode
• What is GitHub Copilot Agent Mode?
• Explore the power of autonomous development assistance
11. Accelerate development with GitHub Copilot cloud agent
• Understanding and enabling the GitHub Copilot Cloud Agent
• Security, risks, and limitations of the Copilot Cloud Agent
• Assigning, tracking, and troubleshooting Copilot Cloud Agent tasks
• Customizing, extending, and validating the Copilot Cloud Agent
• Responsible use of GitHub Copilot Cloud Agent on GitHub.com
12. Introduction to MCP Server
• Simplify your AI workflow with GitHub MCP Server
• Configure, connect, and use GitHub MCP Server in VS Code
• Using GitHub MCP Server with Copilot Chat
13. Leveling up code reviews and pull requests with GitHub Copilot
• What GitHub Copilot adds to the review process
• Using Copilot as a reviewer in GitHub.com
• Catching issues early and automating reviews with Copilot
• Measuring impact and optimizing premium request units (PRUs)
14. Using GitHub Copilot with JavaScript
• What is GitHub Copilot
• Use GitHub Copilot with JavaScript
15. Using GitHub Copilot with Python
• What is GitHub Copilot?
• Use GitHub Copilot with Python
Course Details
• Basic understanding of GitHub fundamentals.
AI Developers and Engineers: Professionals involved in creating and deploying AI systems who need to understand the ethical implications and governance frameworks. Data Scientists and Analysts: Individuals who work with data and AI models, focusing on ensuring transparency, fairness, and accountability in their work. Business Leaders and Managers: Decision-makers who oversee AI projects and need to implement responsible AI practices within their organizations. Policy Makers and Regulators: Those responsible for creating policies and regulations around AI usage, ensuring that AI systems are developed and used ethically and safely.
For online (live) format
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