Python

Documentation Index

Fetch the complete documentation index at: https://ai.tharung.in/llms.txt

Use this file to discover all available pages before exploring further.

​

1. Introduction: Python for AI

• Python is widely used in AI because of:
  • Simple syntax
  • Strong libraries (NumPy, Pandas, TensorFlow, PyTorch)
• Focus is on building a professional setup

​

2. Installing Python

​

Windows

• Download from python.org
• Check “Add Python to PATH”

​

Mac

python3 --version

• Install if not available

​

3. Installing VS Code

• Free and powerful code editor
• Supports extensions for Python and AI work

​

4. VS Code Setup

​

Extensions

• Python (Microsoft)
• Pylance
• Jupyter

​

Setting

• Enable: Run Python in file directory

​

5. Project Setup

• Use kebab-case naming
  • Example: ai-project-demo
• Save as .code-workspace

​

6. First Python File

• Extension: .py
• Example:

print("Hello World")

​

7. Running Python Code

python file.py

​

8. Python Environments

• Use virtual environments (venv)
• Avoid dependency conflicts

python -m venv .venv

​

9. Pip and Packages

• Install libraries using pip

pip install requests

​

10. Using Packages (Your Example)

import requests

response = requests.get('https://api.github.com')
print(response.status_code)

• requests.get() → sends HTTP request
• status_code → shows response status (200 = success)

​

11. Interpreter Selection

• Select correct Python interpreter in VS Code
• Must match your virtual environment

​

12. Jupyter Interactive Mode

pip install ipykernel

• Run code using Shift + Enter
• Helps in debugging and step-by-step execution

​

13. Anaconda (Optional)

• Alternative to pip + venv
• Pre-installed data science libraries

​

14. Final Recap

• Install Python + VS Code
• Add extensions
• Create project
• Setup venv
• Install packages
• Run code and use Jupyter