Mastering GitHub

Master GitHub Enterprise administration across seven critical domains. You will configure identity management using SAML SSO and Enterprise Managed Users with identity providers like Azure Active Directory and Okta. You will enforce the principle of least privilege by setting access permissions at the enterprise, organization, team, and repository levels, assigning roles such as member, owner, maintainer, and triage to match each user's responsibilities. The course covers security compliance through the security tab, dependency graphs, Dependabot alerts, and repository insights for monitoring project health. You will manage GitHub Actions at the enterprise scale using the Actions API to configure allowed actions, self-hosted runners, and workflow policies across organizations. You will also host and distribute software through GitHub Packages, including the container registry, RubyGems, npm, Maven, and NuGet registries. Each module includes hands-on demonstrations in the GitHub interface, from configuring organization settings and billing consoles to compiling Python from source in Codespaces. You will apply Kaizen continuous improvement methodology and the Plan-Do-Check-Act cycle to systematically optimize enterprise workflows.

Build a complete production application from scratch using GitHub Copilot as your AI pair programmer. This capstone course guides you through every phase of real-world software development — from project planning and architecture decisions through API implementation, business logic, data persistence, testing, and code review. You will start by scoping a production project, analyzing domain context, and querying internal knowledge bases to inform your development approach. Then you will implement a multi-layer application: building RESTful API endpoints, implementing data validation and schema enforcement, coding business logic with complex rules and edge cases, and integrating database persistence using ORMs. Finally, you will build comprehensive test suites spanning unit, integration, and end-to-end testing, review your implementation against industry best practices, and reflect on AI-assisted development workflows. Each task uses a Makefile-driven quality pipeline, giving you a production-ready development workflow. This course synthesizes skills from the entire Mastering GitHub Copilot specialization into one cohesive deliverable.

Move beyond basic code completions and learn to use GitHub Copilot as a conversational development partner. This course teaches you how to structure multi-turn interactions that build context incrementally, producing more accurate and relevant code than single-shot prompts. You will master iterative refinement techniques that transform rough initial outputs into production-quality code through structured follow-up prompts and scope narrowing. The course covers the three Copilot interaction modes — Ask, Edit, and Agent — and when to use each for maximum effectiveness. You will learn to control context precisely using chat inputs like @workspace references, #file markers, and open editor tabs. Advanced topics include generating API documentation directly from code, creating implementation code from API specifications, and navigating unknown codebases using structured Copilot conversations. Each technique is demonstrated with real-world projects in Visual Studio Code, giving you practical patterns you can apply immediately to your own development workflow. A capstone project synthesizes all techniques into an end-to-end AI-assisted development scenario.

Learn to build cloud-based development environments with GitHub Codespaces, run GPU-accelerated AI workloads, use GitHub Copilot for AI-assisted coding, and automate CI/CD pipelines with GitHub Actions. This hands-on course walks you through launching Codespaces from repository templates, configuring dev containers for different machine types, and running NVIDIA GPU instances for machine learning tasks. You will use Whisper for speech-to-text transcription on GPU-enabled Codespaces and explore Hugging Face for model hosting, datasets, and fine-tuning pre-trained models. The course demonstrates GitHub Copilot and Copilot Labs for code suggestions, code translation, and conversational development via Copilot Chat. You will also build GitHub Actions workflows using YAML configuration files to automate testing and deployment on Ubuntu containers. By the end, you will integrate Codespaces, Copilot, and Actions into a unified end-to-end development workflow. Each lesson includes live demonstrations inside real GitHub repositories, giving you practical experience with the tools used in professional software teams.

Learn to evaluate, select, and integrate AI models using GitHub Models — a service that provides ready-to-use, off-the-shelf machine learning models directly within the GitHub platform. You will navigate the GitHub Models marketplace to compare models by provider, capability, and rate limits, then test them interactively using the built-in playground with system prompts and temperature controls. This course covers the practical skills needed to move from model evaluation to production integration. You will understand how rate limits work across different models, learn strategies for scaling beyond the free tier through Azure AI integration, and set up cloud development environments using GitHub Codespaces with pre-installed Python libraries. In the final module, you will build a complete HTTP Application Programming Interface (API) using FastAPI that connects to GitHub Models, authenticate using personal access tokens, test your endpoints within Codespaces, and apply validation strategies for production readiness. You will also learn about responsible AI features including content filters that GitHub applies through Azure to ensure safe model interactions. By the end of this course, you will have hands-on experience building and testing AI-powered API endpoints ready for cloud deployment.

Learn to validate, audit, and govern AI-generated code using GitHub Copilot. This course teaches you systematic techniques for catching security vulnerabilities, logical flaws, and hallucinated APIs in Copilot output — skills essential for any team adopting AI-assisted development. You will start by building a validation workflow that combines static analysis, manual review, and security scanning to audit AI-generated code against OWASP patterns. Hands-on challenges walk you through identifying injection vulnerabilities, detecting hallucinated function calls, and documenting remediation steps. The course then covers custom Copilot configurations using copilot-instructions.md, where you define project-specific coding standards that Copilot follows automatically. You will create, test, and iterate on custom rules that enforce team conventions across all generated code. Finally, you will evaluate Large Language Models for development tasks — comparing capabilities across providers like OpenAI, Anthropic, and Google — using performance benchmarks and cost-benefit analysis to select the right model for each coding requirement. By the end of this course, you will have a governance framework for integrating AI code generation into production workflows with confidence.

Learn to secure GitHub accounts, repositories, and organizations using authentication, permissions, and enterprise governance controls. This course teaches you to configure two-factor authentication with TOTP apps and recovery codes, assign repository permission levels following least-privilege principles, and set repository visibility to balance collaboration with security.

Learn Git and GitHub from the ground up, then apply Artificial Intelligence (AI) agents to automate development workflows. This course takes you from installing Git and creating your first repository to submitting pull requests, contributing to open source, and building custom AI agents with Model Context Protocol (MCP). You will start with Git fundamentals: initializing repositories, staging and committing changes, and safely undoing work with revert and reset. From there, you will connect local repositories to GitHub using push, pull, and clone, and learn to collaborate through branching, pull requests, and code review. The course covers community workflows including forking, issue tracking, and automating Continuous Integration and Continuous Delivery (CI/CD) pipelines with GitHub Actions. You will also configure gitignore patterns to keep sensitive files out of version control and write effective README documentation in Markdown. The final module introduces AI agents on GitHub: the progression from code-completion assistants to autonomous agents, how agents interact with repositories and Application Programming Interfaces (APIs) through tool execution, task selection criteria, security boundaries, and building custom agents that connect to external tools via MCP.