Introduction to ML with Python

Introduction to Machine Learning with Python is the companion code repository for the book by Andreas Mueller and Sarah Guido. It provides the Jupyter notebooks used to develop the book, allowing readers to reproduce examples and visualizations. Chapters cover machine-learning foundations, supervised and unsupervised methods, feature engineering, model evaluation, pipelines, and text processing. The included mglearn helper library supplies educational datasets, plotting functions, and figures used throughout the material. Most required datasets are bundled, although the ACL IMDb data must be downloaded separately. Setup instructions cover NumPy, SciPy, scikit-learn, Matplotlib, pandas, Pillow, Graphviz, NLTK, and spaCy. The repository is best treated as a hands-on learning companion whose older dependency assumptions may require adjustment in modern Python environments.