# Personal AI Fluency Plan

## 1. Vision & Goals

| Short‑term (0‑3 months) | Medium‑term (3‑12 months) | Long‑term (1‑3 years) |
|--------------------------|---------------------------|------------------------|
| • Understand core AI concepts (ML, DL, LLMs). | • Build end‑to‑end AI projects (chatbot, recommendation, image‑to‑text). | • Contribute to open‑source AI projects or publish research. |
| • Gain hands‑on experience with a popular LLM (OpenAI, Anthropic, Ollama). | • Master fine‑tuning, prompt engineering, and evaluation. | • Lead AI initiatives in a product or organization. |
| • Create a portfolio of 2‑3 small AI demos. | • Develop a personal AI toolkit (LangChain, Chroma, Ollama). | • Mentor others in AI fluency. |

## 2. Core Competencies (Framework from the course)

1. **Foundations** – math, statistics, data pipelines.
2. **Modeling** – supervised, unsupervised, reinforcement learning.
3. **LLMs & Prompt Engineering** – tokenization, embeddings, chain‑of‑thought.
4. **Deployment & Ops** – Docker, FastAPI, monitoring.
5. **Ethics & Governance** – bias, privacy, safety.
6. **Domain‑Specific** – choose a domain (health, finance, creative). |

## 3. Learning Path

| Week | Focus | Resources | Deliverable |
|------|-------|-----------|-------------|
| 1‑2 | Foundations: Linear algebra, probability | *Khan Academy*, *StatQuest* | Quiz on probability |
| 3‑4 | ML basics: scikit‑learn, decision trees | *Hands‑On Machine Learning with Scikit‑learn* | Predictive model on UCI dataset |
| 5‑6 | Deep Learning: PyTorch basics | *Deep Learning with PyTorch* | Train a CNN on MNIST |
| 7‑8 | LLMs: architecture, tokenization | *OpenAI Cookbook*, *Hugging Face* | Summarize a long article with GPT‑4 |
| 9‑10 | Prompt engineering | *Prompt Engineering Guide*, *Anthropic* | Build a multi‑turn chatbot |
| 11‑12 | Retrieval & RAG | *LangChain*, *Chroma* | Build a RAG system for a knowledge base |
| 13‑14 | Deployment | *FastAPI*, *Docker* | Deploy chatbot as a REST API |
| 15‑16 | Ethics | *AI Ethics* by Microsoft | Write a short policy document |
| 17‑18 | Domain project | Choose domain | End‑to‑end AI product |

## 4. Tools & Stack

| Tool | Purpose | Install |
|------|---------|---------|
| Python 3.10+ | Core language | `python -m venv venv && source venv/bin/activate` |
| LangChain | Orchestration | `pip install langchain langchain_text_splitters` |
| Chroma | Vector store | `pip install chromadb` |
| Ollama | Local LLMs | `pip install langchain_ollama` |
| FastAPI | API framework | `pip install fastapi uvicorn` |
| Docker | Containerization | `docker` command line |

## 5. Practice & Portfolio

1. **Mini‑projects** – 2‑3 demos (chatbot, image captioner, recommendation). |
2. **Blog posts** – explain each project, share code on GitHub. |
3. **Open‑source contribution** – submit PRs to LangChain or Hugging Face. |
4. **Community** – join Discord, Reddit, or local meetups. |

## 6. Evaluation & Feedback

- **Weekly check‑ins** – self‑assessment, peer review. |
- **Monthly demo** – showcase progress to a mentor or community. |
- **Quarterly review** – adjust goals, add new skills. |

## 7. Timeline (Gantt‑style)

```
Week 1‑2   Foundations
Week 3‑4   ML basics
Week 5‑6   Deep Learning
Week 7‑8   LLMs
Week 9‑10  Prompt Engineering
Week 11‑12 Retrieval & RAG
Week 13‑14 Deployment
Week 15‑16 Ethics
Week 17‑18 Domain Project
```

## 8. Next Steps

1. Set up a GitHub repo for the portfolio. |
2. Create a virtual environment and install dependencies. |
3. Start with the Foundations module. |
4. Track progress in a Notion page or spreadsheet. |

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**Note**: This plan is a living document. Feel free to adapt it to your interests, time availability, and emerging AI trends.