81 lines
3.8 KiB
Markdown
81 lines
3.8 KiB
Markdown
# Personal AI Fluency Plan
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## 1. Vision & Goals
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| Short‑term (0‑3 months) | Medium‑term (3‑12 months) | Long‑term (1‑3 years) |
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|--------------------------|---------------------------|------------------------|
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| • 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. |
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| • 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. |
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| • Create a portfolio of 2‑3 small AI demos. | • Develop a personal AI toolkit (LangChain, Chroma, Ollama). | • Mentor others in AI fluency. |
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## 2. Core Competencies (Framework from the course)
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1. **Foundations** – math, statistics, data pipelines.
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2. **Modeling** – supervised, unsupervised, reinforcement learning.
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3. **LLMs & Prompt Engineering** – tokenization, embeddings, chain‑of‑thought.
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4. **Deployment & Ops** – Docker, FastAPI, monitoring.
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5. **Ethics & Governance** – bias, privacy, safety.
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6. **Domain‑Specific** – choose a domain (health, finance, creative). |
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## 3. Learning Path
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| Week | Focus | Resources | Deliverable |
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|------|-------|-----------|-------------|
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| 1‑2 | Foundations: Linear algebra, probability | *Khan Academy*, *StatQuest* | Quiz on probability |
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| 3‑4 | ML basics: scikit‑learn, decision trees | *Hands‑On Machine Learning with Scikit‑learn* | Predictive model on UCI dataset |
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| 5‑6 | Deep Learning: PyTorch basics | *Deep Learning with PyTorch* | Train a CNN on MNIST |
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| 7‑8 | LLMs: architecture, tokenization | *OpenAI Cookbook*, *Hugging Face* | Summarize a long article with GPT‑4 |
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| 9‑10 | Prompt engineering | *Prompt Engineering Guide*, *Anthropic* | Build a multi‑turn chatbot |
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| 11‑12 | Retrieval & RAG | *LangChain*, *Chroma* | Build a RAG system for a knowledge base |
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| 13‑14 | Deployment | *FastAPI*, *Docker* | Deploy chatbot as a REST API |
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| 15‑16 | Ethics | *AI Ethics* by Microsoft | Write a short policy document |
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| 17‑18 | Domain project | Choose domain | End‑to‑end AI product |
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## 4. Tools & Stack
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| Tool | Purpose | Install |
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|------|---------|---------|
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| Python 3.10+ | Core language | `python -m venv venv && source venv/bin/activate` |
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| LangChain | Orchestration | `pip install langchain langchain_text_splitters` |
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| Chroma | Vector store | `pip install chromadb` |
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| Ollama | Local LLMs | `pip install langchain_ollama` |
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| FastAPI | API framework | `pip install fastapi uvicorn` |
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| Docker | Containerization | `docker` command line |
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## 5. Practice & Portfolio
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1. **Mini‑projects** – 2‑3 demos (chatbot, image captioner, recommendation). |
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2. **Blog posts** – explain each project, share code on GitHub. |
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3. **Open‑source contribution** – submit PRs to LangChain or Hugging Face. |
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4. **Community** – join Discord, Reddit, or local meetups. |
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## 6. Evaluation & Feedback
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- **Weekly check‑ins** – self‑assessment, peer review. |
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- **Monthly demo** – showcase progress to a mentor or community. |
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- **Quarterly review** – adjust goals, add new skills. |
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## 7. Timeline (Gantt‑style)
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```
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Week 1‑2 Foundations
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Week 3‑4 ML basics
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Week 5‑6 Deep Learning
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Week 7‑8 LLMs
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Week 9‑10 Prompt Engineering
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Week 11‑12 Retrieval & RAG
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Week 13‑14 Deployment
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Week 15‑16 Ethics
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Week 17‑18 Domain Project
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```
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## 8. Next Steps
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1. Set up a GitHub repo for the portfolio. |
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2. Create a virtual environment and install dependencies. |
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3. Start with the Foundations module. |
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4. Track progress in a Notion page or spreadsheet. |
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---
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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. |