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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

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