---

🤖 The Only Python Course You Need in the AI Era

中文版 | Español | Quick Start | Curriculum | Tech Stack

60 lessons. Zero prerequisites. One goal: make you a Python developer who builds real AI applications.

---

🎯 Course Philosophy

Most Python courses teach you to print "Hello, World!" and move on. This course is different.

Every concept is taught through an AI lens:

• Instead of "learn variables" → "parse an LLM API response into variables"
• Instead of "learn lists" → "process a batch of AI-generated outputs"
• Instead of "learn file I/O" → "save and load conversation histories"
• Instead of "learn classes" → "build reusable AI pipeline components"

By the end, you won't just know Python — you'll be able to build AI applications that actually work.

---

✨ Highlights

🎓 60 Lessons	From "Hello World" to production AI apps
🤖 AI-First	Every exercise uses AI context and real-world patterns
🔨 Project-Based	Build real things: chatbots, agents, APIs, data pipelines
📦 Modern Stack	OpenAI SDK, LangChain, FastAPI, Pandas, NumPy
🆓 No API Key Needed	All exercises run with mock clients — swap in real API when ready
🔰 Zero Prerequisites	Truly beginner friendly — no prior programming experience required

---

🚀 Quick Start

1. Get the Code

git clone https://github.com/unrealandychan/learn-python-with-ai.git
cd learn-python-with-ai

2. Set Up Python (3.11+)

# Check your version
python --version   # Should be 3.11 or higher

# If not installed: https://www.python.org/downloads/

3. Create a Virtual Environment

# Using uv (recommended — fastest)
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv && source .venv/bin/activate

# OR using standard pip
python -m venv .venv && source .venv/bin/activate  # macOS/Linux
python -m venv .venv && .venv\Scripts\activate      # Windows

4. Install Dependencies

pip install -r requirements.txt

5. Start Learning!

# Start with lesson 1
cd lesson_01_intro_to_python
python exercise.py

6. (Optional) Add Your OpenAI API Key

# Create a .env file
echo "OPENAI_API_KEY=sk-your-key-here" > .env

Note: All lessons work with mock AI clients — you don't need an API key to learn. Add one when you're ready to use the real APIs.

---

📚 Curriculum

🟢 Module 1: Python Foundations (Lessons 1–20)

Core Python with an AI context — understand every concept in terms of how it's used in AI development.

Lesson	Topic	AI Application
01	Intro to Python	Why Python dominates AI
02	Variables & Data Types	Storing LLM responses in variables
03	Basic Operators	Processing API response scores
04	User Input & Type Casting	Building interactive AI tools
05	Conditional Statements	Routing based on AI classification results
06	Lists	Storing multiple AI-generated items
07	List Methods	Managing conversation message arrays
08	For Loops	Iterating over batch AI results
09	While Loops	Retry logic for API calls
10	Dictionaries	Parsing JSON API responses
11	Tuples & Sets	Deduplicating AI outputs
12	Defining Functions	Reusable AI helper functions
13	Function Arguments & Returns	Building flexible API wrappers
14	Variable Scope	Managing API keys and config
15	Modules & Importing	Using the AI library ecosystem
16	File I/O: Reading	Loading prompts and knowledge bases
17	File I/O: Writing	Saving AI outputs to disk
18	Error Handling	Robust API call error handling
19	OOP Intro	Modeling AI conversations as objects
20	Next Steps	Mini project: command-line AI assistant

🟡 Module 2: Advanced Python (Lessons 21–40)

Advanced patterns that every AI engineer uses daily.

Lesson	Topic	AI Application
21	OOP Inheritance	Specialized AI model clients
22	OOP Polymorphism	Interchangeable AI providers
23	OOP Encapsulation	Protecting API keys in classes
24	OOP Dunder Methods	Custom AI response objects
25	Static & Class Methods	Shared configuration across AI clients
26	List Comprehensions	Transforming batches of AI outputs
27	Dict & Set Comprehensions	Aggregating AI result metrics
28	Lambda Functions	Inline transformation of AI data
29	Map, Filter, Reduce	Functional pipelines for AI data
30	Generators	Memory-efficient streaming of AI responses
31	Decorators	Rate limiting, logging, caching AI calls
32	Collections Module	Counting tokens, frequency analysis
33	Dates & Times	Timestamping AI interactions
34	JSON Data	Parsing LLM JSON output
35	OS & Sys Modules	File system operations for AI pipelines
36	Multithreading	Parallel AI API calls
37	Multiprocessing	CPU-bound AI preprocessing
38	Asyncio Intro	Understanding async AI workflows
39	Async/Await	Async LLM calls with streaming
40	Advanced Project	Build an async AI pipeline

🔵 Module 3: Essential Libraries (Lessons 41–53)

The libraries that power real-world Python and AI applications.

Lesson	Topic	AI Application
41	Requests	Calling REST APIs and LLM endpoints
42	BeautifulSoup4	Scraping training data from the web
43	Pandas	Data preparation for ML models
44	Matplotlib	Visualizing AI model metrics
45	Seaborn	Statistical visualization for model analysis
46	FastAPI	Serving AI models as REST APIs
47	Git & GitHub	Version-controlling your AI projects
48	Pytest	Testing AI application components
49	Ruff	Code quality for production AI code
50	UV Dependency Management	Managing AI project dependencies
51	Databases	Storing AI conversation history
52	Config Management	Secure API key management with .env
53	Python for MCP & Skills	Building tool-calling infrastructure

🔴 Module 4: AI & LLM Integration (Lessons 54–60)

The cutting edge — calling, prompt-engineering, and building with LLMs.

Lesson	Topic	AI Application
54	OpenAI SDK	Chat completions, streaming, function calling, JSON mode
55	Prompt Engineering	Few-shot prompting, CoT, output formatting, templates
56	LangChain Basics	LCEL chains, memory, output parsers, RAG pattern
57	NumPy for AI	Vector math, cosine similarity, softmax, embeddings
58	AI Agents	ReAct pattern, tool registries, the agent loop
59	Vector Embeddings	Semantic search, simple vector DB, chunking
60	Capstone Project	Build a complete AI application from scratch

---

🛠 Tech Stack

This course uses the modern Python AI ecosystem:

Category	Libraries
LLM APIs	openai, langchain, langchain-openai
Data Science	numpy, pandas, matplotlib, seaborn
Web / APIs	fastapi, httpx, requests, uvicorn
Config	python-dotenv
Dev Tools	pytest, ruff, uv
Databases	sqlalchemy
Scraping	beautifulsoup4

---

📋 Prerequisites

None. This course starts from absolute zero.

You need:

• A computer (Windows, macOS, or Linux)
• Internet connection
• Curiosity and willingness to learn

You do NOT need:

• Prior programming experience
• Math background
• An OpenAI API key (all exercises work with mock clients)

---

📁 Lesson Structure

Every lesson follows the same structure:

lesson_XX_topic_name/
├── instructions.md    ← Read this first: theory, examples, concepts
├── exercise.py        ← Your playground: complete the TODO exercises
└── solution.py        ← Check your work after trying yourself

How to use each lesson:

1. 📖 Read instructions.md from top to bottom
2. ✏️ Open exercise.py and complete the # YOUR CODE HERE sections
3. 🚀 Run your file: python exercise.py
4. 🔍 Compare with solution.py if you're stuck

---

💡 Learning Tips

• Type the code — don't copy-paste. Typing builds muscle memory.
• Break things — modify the examples and see what happens.
• Use AI as a tutor — ask ChatGPT or Copilot to explain concepts you find confusing.
• Don't skip — each lesson builds on the previous ones.
• Build something — after each module, try building a small project with what you've learned.

---

🗺 Learning Path

Weeks 1-4:   Lessons 01-20  → Python Foundations
Weeks 5-8:   Lessons 21-40  → Advanced Python
Weeks 9-12:  Lessons 41-53  → Essential Libraries  
Weeks 13-15: Lessons 54-60  → AI & LLM Integration

At ~4 lessons/week, you'll complete the course in ~15 weeks. Take your time — depth over speed.

---

🤝 Contributing

Found a bug? Have an improvement? Pull requests are welcome!

1. Fork the repo
2. Create a branch: git checkout -b fix/lesson-42-typo
3. Make your changes
4. Push and open a PR

---

📄 License

MIT License — free to use, share, and modify.

---

Happy learning! Build something amazing. 🚀

If this course helped you, please ⭐ star the repo — it helps others find it!