Featuring structured course notes, hands-on implementations, certificates, and production-style practical work
This repository is my Hugging Face AI Learning Portfolio — a structured record of the courses I completed, the practical implementations I built, the certifications I earned, and the technical workflows I documented along the way.
It is designed to show more than course completion.
It documents:
- ✅ completed Hugging Face course learning tracks
- ✅ practical implementations and code artifacts
- ✅ benchmark-style and automation-oriented hands-on work
- ✅ certificates and learning milestones
- ✅ screenshots, notes, references, and setup documentation
- ✅ a consistent portfolio-style presentation of technical growth
This repository is intentionally built as a documentation-first learning portfolio rather than a random collection of files.
courses/— full course documentation hubcourses/agents-course/— Hugging Face Agents Coursecourses/llm-course/— Hugging Face LLM Coursecourses/mcp-course/— Hugging Face MCP Coursecertificates/— certificate archiveassets/— shared visuals and supporting assetsroadmap/— post-course expansion ideas and future portfolio direction
This repository currently covers three major Hugging Face learning tracks:
Documented with:
- course-level overview
- final project implementation
- benchmark-related work
- certificates
- screenshots and supporting artifacts
Core themes covered:
- AI agent fundamentals
- reasoning and action workflows
- tool use
smolagentsLlamaIndexLangGraph- Agentic RAG
- GAIA-style evaluation workflow
Documented with:
- course overview
- foundational LLM unit documentation
- certificate
- unit-level learning notes
Core themes covered:
- fundamentals of Large Language Models
- transformer architecture
- self-attention
- pretrained model usage
- core NLP task understanding
- Hugging Face ecosystem basics
- model limitations and bias awareness
Documented with:
- fundamentals of MCP
- end-to-end MCP application work
- EC2-based practical server/client setup
- Continue CLI integration
- production-style MCP automation workflow
- GitHub Actions and Slack integration
- screenshots, implementation files, notes, and certificate artifacts
Core themes covered:
- Model Context Protocol fundamentals
- host / client / server architecture
- protocol and capability design
- Gradio MCP server and client patterns
- remote MCP connectivity
- FastMCP workflow development
- webhook-driven automation
- Slack alerting and CI/CD communication flows
A final benchmark-oriented project from the Agents Course, built around a Hugging Face Space-based submission workflow.
Includes:
app.pyrequirements.txt- project README
- final score screenshot
📂 courses/agents-course/unit4-gaia-evaluation-agent/
A practical MCP workflow built with a Gradio MCP server and multiple client-side approaches.
Includes:
- Gradio MCP server
- client configs
- Continue CLI client setup
- Gradio MCP client
- Tiny Agents integration
- screenshots and EC2 notes
📂 courses/mcp-course/unit2-end-to-end-mcp-application/
A production-style MCP workflow server built around PR analysis, GitHub Actions event handling, and Slack notifications.
Includes:
- FastMCP workflow server implementations
- GitHub webhook ingestion
- CI status prompts
- Slack notification workflow
- Claude Code integration flow
- screenshots and certificate
📂 courses/mcp-course/unit3-mcp-for-production-automation/
Foundational LLM learning material from the Hugging Face LLM Course, documented in a portfolio-friendly structure.
Includes:
- unit README
- core transformer and NLP concepts
- certificate placement
- course positioning inside the broader portfolio
📂 courses/llm-course/unit1-fundamentals-of-llms/
Documented learning areas include:
- what AI agents are
- how LLMs operate as agent “brains”
- tools and actions
- Think → Act → Observe workflows
smolagentsLlamaIndexLangGraph- Agentic RAG
- benchmark-driven final project work
Documented learning areas include:
- what LLMs are
- transformer architecture
- self-attention
- pretrained models
- NLP tasks
- Hugging Face libraries and ecosystem
- bias, hallucinations, and limitations
Documented learning areas include:
- MCP fundamentals
- host/client/server roles
- transports and protocol concepts
- tools, resources, prompts, and sampling
- Gradio MCP applications
- client/server connectivity
- Continue and CLI-based experimentation
- workflow servers for real automation
- GitHub Actions + Slack notification pipelines
| Track | Status | Portfolio Coverage |
|---|---|---|
| Hugging Face Agents Course | ✅ Completed | Course overview, final project, certificates, benchmark evidence |
| Hugging Face LLM Course | ✅ Completed | Course overview, foundational unit documentation, certificate |
| Hugging Face MCP Course | ✅ Completed | Unit 1 fundamentals, Unit 2 application work, Unit 3 automation workflow |
Hugging-Face-AI-Learning-Portfolio/
├── assets/
│ ├── Hf-logo-with-title.svg
│ └── README.md
│
├── certificates/
│ ├── README.md
│ ├── agents-course/
│ └── llm-course/
│
├── courses/
│ ├── README.md
│ ├── agents-course/
│ │ ├── README.md
│ │ └── unit4-gaia-evaluation-agent/
│ │ ├── README.md
│ │ ├── app.py
│ │ ├── requirements.txt
│ │ └── screenshots/
│ │
│ ├── llm-course/
│ │ ├── README.md
│ │ └── unit1-fundamentals-of-llms/
│ │ └── README.md
│ │
│ └── mcp-course/
│ ├── README.md
│ ├── unit1-fundamentals-of-mcp/
│ ├── unit2-end-to-end-mcp-application/
│ └── unit3-mcp-for-production-automation/
│
├── roadmap/
│ └── README.md
│
├── .gitignore
└── README.md
This repository is meant to show that I can do more than “finish a course.”
It demonstrates that I can:
- study technical material in depth
- convert learning into practical implementations
- work across agents, LLMs, and MCP workflows
- build and document code-based outputs
- troubleshoot real environment and integration issues
- present learning in a portfolio-safe, professional format
- maintain consistent documentation across multiple technical tracks
This repository contains both centralized certificates and unit-level evidence.
Examples include:
- Agents Course certificates
- LLM certificate
- MCP unit-level certificate artifacts
- benchmark screenshots
- implementation screenshots
- workflow screenshots from practical MCP automation exercises
Certificate files are stored in:
certificates/- and in selected unit folders where the artifact is tightly tied to the implementation context
- Start with
courses/README.md - Open the course folder you want to review
- Read the course-level README first
- Move to the most implementation-heavy unit folder
- Inspect screenshots, code, and notes together for full context
-
For agent systems and benchmark-style work:
courses/agents-course/ -
For LLM foundations:
courses/llm-course/ -
For systems integration and automation workflows:
courses/mcp-course/
This portfolio is intentionally built around three ideas:
I want the work behind the learning to be visible — not just the outcome.
A strong technical portfolio is easier to understand when the work is documented clearly and consistently.
Hands-on implementations, configs, screenshots, notes, and setup lessons are valuable artifacts and worth keeping in a reusable form.
The main course-learning phase documented in this repository is now complete.
That means this portfolio is no longer centered on “what I plan to study next” inside these Hugging Face tracks. Instead, it now serves as a strong foundation for the next phase:
- deeper standalone AI builds
- extensions of MCP and agent workflows
- more polished project packaging
- independent experimentation beyond course structure
- stronger production-style automation projects
The roadmap/ directory can now be used as a next-phase project and experimentation direction, not as a placeholder for incomplete course work.
This repository captures a full Hugging Face learning journey across Agents, LLMs, and MCP in a way that is structured, practical, and portfolio-ready.
It is both:
- a record of what I completed
- and a base I can continue building from through deeper real-world AI engineering work
Abdul Rehman
Cybersecurity • Linux • AWS • AI Automation • AI Agents • LLM & MCP Learning Portfolio
