scikit-learn-videos

Why we included this project

Ten Jupyter notebooks walk through the same ground as a 4.5-hour video series, one notebook per lesson, so you can work through each example in your own environment instead of just watching. The material builds naturally, starting with the iris dataset and moving through model evaluation, and the author has updated the code for Python 3.9 and scikit-learn 0.23 while keeping the original Python 2.7 versions in an archive branch. It works well as a refresher for people who already know train/test splits, cross-validation, and feature selection, and as a structured introduction for newcomers to the library. It is learning material rather than something you deploy, but for anyone who wants to pick up scikit-learn through worked examples, it is a solid companion.