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Course Outline
Overview of AI Coding Assistants
- Defining the nature and scope of AI coding assistants.
- Tracing the historical evolution of AI within the software development sector.
- Evaluating the advantages and inherent limitations of AI coding assistants.
Underlying Technologies of AI Coding Assistants
- A foundational look at machine learning and natural language processing.
- Introduction to the algorithms powering code generation.
- Integrating AI capabilities with existing development toolchains.
Surveying Leading AI Coding Assistant Platforms
- An overview of prominent tools such as GitHub Copilot and IntelliCode.
- Practical sessions focusing on core functionalities.
- A comparative assessment of distinct tool sets.
Integrating into Basic Workflows
- Configuring an AI coding assistant within your Integrated Development Environment (IDE).
- Leveraging AI assistance for straightforward coding challenges.
- Personalizing assistant settings to align with specific project requirements.
Ethics and Responsible Application
- Examining issues of bias and fairness within AI tools.
- Establishing basic protocols for responsible usage.
- Addressing critical privacy and security implications.
Practical Project Application
- Implementing an AI coding assistant in a small-scale project.
- Conducting peer reviews and gathering feedback.
- Discussing opportunities for improvement and key takeaways.
Wrap-up and Future Directions
Requirements
- Foundational knowledge of software development principles.
- Prior experience with at least one programming language (such as Python or JavaScript).
Target Audience
- Software developers.
- Product managers.
- Technical team leads.
14 Hours
Testimonials (1)
The way you use the copilot, more rule more close to what you need.
