This repository contains the complete code and supplementary materials for the book "Machine Learning Made Visual with Python." The book provides a hands-on, visual, and intuitive approach to learning classical machine learning algorithms, combining clear illustrations with Python implementations to bridge the gap between theory and practice.
The content covers foundational topics in machine learning and mathematics for machine learning. It explains supervised learning tasks, including regression and classification, and unsupervised learning tasks, such as dimensionality reduction and clustering. Each algorithm is introduced with intuition, visual explanations, and practical Python examples. Mathematical concepts such as linear algebra, probability, statistics, and optimization are integrated into the learning process to deepen understanding.
All code is provided as Jupyter Notebooks, enabling interactive learning and experimentation. The book ONLY focuses on classical machine learning methods, offering a solid foundation for future exploration of advanced topics like neural networks and large language models.
To get started, install Anaconda (https://anaconda.com/), which includes Python, Jupyter Notebook, and JupyterLab for interactive coding and visualization. Official guides and library tutorials are recommended for reference, including NumPy, Pandas, Matplotlib, and scikit-learn.
This book is intended for students, educators, developers, and self-learners seeking a visual and practical understanding of machine learning. The accompanying code repository allows readers to experiment, visualize, and reinforce their learning effectively.
Repository link: https://github.com/visualize-ml/Machine-Learning-Made-Visual-with-Python
Anaconda download: https://www.anaconda.com/
JupyterLab guide: https://jupyterlab.readthedocs.io/en/stable/getting_started/overview.html
NumPy guide: https://numpy.org/doc/stable/user/quickstart.html
Pandas tutorial: https://pandas.pydata.org/docs/user_guide/10min.html
Matplotlib tutorials: https://matplotlib.org/stable/tutorials/index.html
Scikit-learn guide: https://scikit-learn.org/stable/user_guide.html