Machine Learning with PyTorch and Scikit-Learn is a code repository accompanying a published machine learning textbook.
The repository contains Jupyter notebooks that implement the code examples from the book, which covers machine learning fundamentals through advanced deep learning techniques. The material progresses from classical algorithms like classification and regression using scikit-learn, through data preprocessing and model evaluation, to modern approaches including convolutional neural networks, recurrent neural networks, transformers, generative adversarial networks, graph neural networks, and reinforcement learning using PyTorch. The notebooks are organized by chapter, with each chapter addressing a specific machine learning topic or technique.
This repository is most useful for readers working through the published book, as the notebooks are designed as supplements to the text rather than standalone learning materials. The README explicitly notes that the notebooks may not be useful without the formulae and descriptive text from the book itself. Someone adopting this should expect to use it alongside the book rather than as an independent resource. The repository includes setup instructions for the coding environment and guidance on running examples in Google Colab, making it accessible across different development setups.
The project maintains the code examples in a stable state aligned with the published book content. Updates appear focused on ensuring the notebooks remain functional with current library versions rather than expanding the material or adding new chapters beyond the book's scope.
