Python 0 to AI Agent — Learning Roadmap

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🎯 Goal

Build a complete, hands-on learning path from zero programming experience to creating an AI Agent using LangChain or PydanticAI.
This roadmap guides learners through fundamentals, tooling, architecture, backend development, and LLM integration — the way modern Python engineers actually work.

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Overview

Phase	Focus	Outcome
0️⃣	Environment & Tools	Set up Python, IDE, Git, uv, Docker
1️⃣	Python Foundations	Master syntax, functions, classes
2️⃣	Working with Data	APIs, JSON, CSV, error handling
3️⃣	Architecture & Patterns	Clean Code, SOLID, design patterns
4️⃣	Modern Backend	FastAPI, async, Pydantic, Docker
5️⃣	AI Agents	Build LangChain/PydanticAI agents
6️⃣	User Interfaces	CLI, GUI, HTML templates, React intro
7️⃣	Growth Path	Next steps: async, cloud, open source

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⚙️ Tech Stack

• Language: Python 3.11+ (via pyenv)
• Environment Manager: uv
• IDE: PyCharm (recommended) or VS Code
• Backend Framework: FastAPI
• Validation: Pydantic
• Containers: Docker
• AI Libraries: LangChain / PydanticAI
• Testing: pytest
• Version Control: Git + GitHub

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🧱 Folder Structure

📁 python-ai-learning/
├── 00_env/              # Environment setup (pyenv, uv, git, docker)
├── 01_basics/           # Python syntax, functions, loops, OOP
├── 02_data_api/         # JSON, CSV, requests, logging, dotenv
├── 03_architecture/     # SOLID, patterns, clean code, tests
├── 04_backend/          # FastAPI, async, Docker
├── 05_ai_agent/         # LangChain, PydanticAI projects
├── 06_ui_interfaces/    # CLI, GUI, HTML templates, React intro
├── 07_next_steps/       # Cloud, CI/CD, Open Source
├── README.md
└── requirements.txt / pyproject.toml

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🧠 Learning Phases

🧩 Phase 0 — Environment & Tools

• Set up IDE (PyCharm or VS Code)
• Install Python via pyenv
• Manage dependencies via uv
• Initialize Git and GitHub repo
• Learn basic Docker commands

See: 0.md

📘 Real Python – Python Development Environments 📹 Tech With Tim – Python Setup for Beginners (0:00–30:00)

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🧩 Phase 1 — Python Foundations

• Variables, data types, loops, functions
• Classes, methods, and objects
• Type hints and dataclass
• DRY / KISS / Zen of Python

See: 1.md

📘 Official Python Tutorial 📹 freeCodeCamp – Python Full Course (0:00–2:00:00)

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🧩 Phase 2 — Working with Data and APIs

• Files: JSON, CSV
• requests, API calls, error handling
• Logging and environment variables
• Small project: Weather Data Fetcher

📘 requests Library 📹 Corey Schafer – Working with JSON (0:00–10:00)

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🧩 Phase 3 — Architecture & Design Patterns

• SOLID, Clean Code
• Observer, Factory, Iterator patterns
• Profiling & optimization (timeit, lru_cache)
• Unit and integration tests with pytest

📘 Refactoring.Guru – Design Patterns in Python 📹 ArjanCodes – SOLID Principles

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🧩 Phase 4 — Backend Development

• FastAPI + Pydantic
• Async programming (async/await)
• Backend design patterns (Repository, Dependency Injection, Factory, Middleware)
• Dockerfile + docker-compose
• Simple REST API project

📘 FastAPI Docs 📘 FastAPI Dependency Injection 📹 FastAPI Crash Course (0:00–40:00) 📹 ArjanCodes – FastAPI Best Practices — watch 0:00–15:00

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🧩 Phase 5 — Building AI Agents

• Introduction to LLMs (OpenAI, local models)
• LangChain basics: Chains, Tools, Memory
• PydanticAI basics: validation, @ai_function
• RAG (Retrieval Augmented Generation) with vector databases
• MCP (Model Context Protocol) for tool integration
• Agent graphs and workflows (LangGraph)
• AI Agent Project: Smart Assistant with Memory and RAG

📘 LangChain Quickstart 📘 LangChain RAG Tutorial 📘 Model Context Protocol 📹 Build AI Agents with PydanticAI (0:00–25:00) 📹 LangGraph Tutorial — watch 0:00–20:00

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🧩 Phase 6 — User Interfaces

• CLI tools with argparse, click, typer
• Python GUI with tkinter (basics)
• HTML generation with Jinja2 templates
• FastAPI serving HTML pages
• Introduction to JavaScript frameworks (React, Vue)
• Understanding Python backend + JS frontend architecture
• Deploy to AWS / Azure

📘 Typer Documentation 📘 Jinja2 Templates 📘 React Official Tutorial 📹 Corey Schafer – argparse Tutorial (0:00–12:00) 📹 freeCodeCamp – Tkinter Course (0:00–30:00)

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🧩 Phase 7 — Next Steps

• Async pipelines, Celery, queues
• Databases: SQL, NoSQL
• Vector DBs (Chroma, Milvus)
• CI/CD
• AI-powered coding tools (GitHub Copilot, Claude Code, Cursor)
• AI development frameworks (Spec Kit, LangSmith, LangFuse)

📘 Celery Documentation 📘 Claude Code Documentation 📘 LangSmith for AI Development 📹 TechWorld with Nana – CI/CD Explained

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🧩 Example Projects

Project	Description
Typing Calculator	Basic Python with dataclass and tests
Weather Fetcher	API + JSON + logging
EventBus	Design pattern (Observer)
FastAPI Service	Async REST backend
Smart Assistant	AI Agent with memory, RAG, and MCP tools

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✅ Learning Outcomes

By the end of this roadmap, you will:

• Write, test, and debug clean Python code
• Use virtual environments, uv, and Git properly
• Understand architecture and design principles
• Build and containerize modern web APIs
• Create working AI agents that interact with data
• Know where to grow next (async, cloud, open source)