This repository documents my work through Introduction to Machine Learning with Python: A Guide for Data Scientists by Andreas C. Müller and Sarah Guido.
I also use it as a sandbox for practice and experimentation, so some of the code goes beyond the material covered in the book. Although the book was published in 2017, it remains an excellent resource. The examples in this repository use more recent versions of the libraries than the original code.
The project currently uses:
- Python 3.11.9
- Virtual environment:
env/venv - Dependencies:
env/requirements.txt
Create the virtual environment:
python -m venv env/venvActivate it on Windows PowerShell:
.\env\venv\Scripts\Activate.ps1On macOS or Linux, activate it with:
source env/venv/bin/activateInstall the dependencies and start JupyterLab:
python -m pip install -r env/requirements.txt
jupyter labThe public repository is organized into the following directories:
projects/- independent projects used to apply and extend the conceptsdata/- datasets used by notebooks and projectsenv/- the dependency specification and local virtual environment
I keep the supplementary materials provided by the book's authors in a local book-materials/ directory. I use them as a reference, but they are not part of the public repository.
I use an AI coding agent as a coach, teacher, code reviewer, and pair-programming partner. Its role is to challenge my reasoning and help me develop my programming and machine learning skills—not to do the learning for me. I remain responsible for understanding and validating everything added to this repository.
This is an active learning project. I update it as I explore new topics, run experiments, and build new projects.
This repository does not currently have a license.