O'Reilly Live Training - Automate Tasks with Python + AI

Quick Setup

1. Install UV

Linux/macOS:

curl -LsSf https://astral.sh/uv/install.sh | sh

Windows (PowerShell):

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

2. Clone and Setup Project

Linux/macOS:

git clone https://github.com/EnkrateiaLucca/oreilly-python-course
cd oreilly-python-course
uv sync
source .venv.bin/activate
uv run ipython kernel install --user --env VIRTUAL_ENV $(pwd)/.venv --name=oreilly-automate-py
playwright install
echo "✅ Setup complete! To execute the jupyter environment for the interactive notebooks run:"
uv run --with jupyter jupyter lab

Windows (PowerShell):

git clone https://github.com/EnkrateiaLucca/oreilly-python-course
cd oreilly-python-course
uv sync
source .venv.bin/activate
uv run ipython kernel install --user --env VIRTUAL_ENV "$PWD\.venv" --name=oreilly-automate-py
playwright install
Write-Output "✅ Setup complete! To execute the jupyter environment for the interactive notebooks run:"
uv run --with jupyter jupyter lab

3. Start Jupyter Lab

# Make sure you're in the project directory
uv run --with jupyter jupyter lab

API Setup

Get your API keys:

1. OpenAI API key
2. Anthropic API key

Setup your .env file

Change the .env.example file to .env and add your API keys:

OPENAI_API_KEY=<your openai api key>
ANTHROPIC_API_KEY=<your claude api key>

What's Included

Dependencies installed:

• AI Libraries: openai, anthropic, ollama
• Data Science: pandas, matplotlib
• Web Scraping: requests, beautifulsoup4, playwright
• Jupyter: jupyterlab, ipykernel, ipywidgets

Course Notebooks

This directory contains all the Jupyter notebooks for the O'Reilly Live Training course organized into logical sections.

Structure

📚 notebooks/01-python-fundamentals/

Core Python concepts and syntax:

• 01-python-basics.ipynb – Python basics: numbers, strings, variables, operators
• 02-working-with-data.ipynb – Lists, dictionaries, file I/O, CSV handling
• 03-packages-apis.ipynb – Using libraries and working with APIs
• file.txt – Sample text file for file operations
• places_to_go.csv – Example CSV data for exercises

🤖 02-ai-apis/

Working with AI services:

• 01-ai-apis-overview.ipynb - Introduction to AI APIs
• 02-ai-tools-hands-on.ipynb - Practical AI tool usage

⚙️ 03-automation-projects/

Real-world automation projects:

• 01-file-management-automation.ipynb - Organizing and managing files
• 02-data-extraction-with-ai.ipynb - Extracting data using LLMs
• 03-web-data-extraction.ipynb - Web scraping and data collection
• 04-data-analysis-automation.ipynb - Automated data analysis
• 05-presentation-automation.ipynb - Generating slides automatically
• 06-browser-automation.ipynb - Controlling web browsers
• 07-workflow-automation.ipynb - Building automation workflows
• 08-email-assistant.ipynb - Email automation and processing
• 09-ai-scheduler-agent.ipynb - Intelligent scheduling systems
• 10-receipt-data-extraction.ipynb - Processing receipts and invoices
• 11-custom-automation-scripts.ipynb - Building your own scripts
• 12-practical-examples.ipynb - Additional real-world examples

📝 04-exercises/

Practice problems and solutions:

• 01-data-types-and-variables.ipynb - Basic Python practice
• 02-functions.ipynb - Function creation exercises
• 03-conditionals-and-files.ipynb - Logic and file handling
• 04-day1-recap.ipynb - Summary and review

📁 assets/

Supporting files, images, sample data, and resources used throughout the course.

Getting Started

1. Navigate to 01-python-fundamentals/ if you're new to Python
2. Work through notebooks in numerical order within each section
3. Use 04-exercises/ to practice what you've learned
4. Move to 02-ai-apis/ and 03-automation-projects/ for advanced topics

Tips

• Each notebook is self-contained but builds on previous concepts
• Sample data and resources are in the assets/ folder
• Run notebooks in order for the best learning experience