task-69970ff6d6d3a5544a3def7a/plan.md

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.