Welcome to Data Mining Course

Welcome to Data Mining Course#

Mahmood Amintoosi, Spring 2025

Computer Science Dept, Ferdowsi University of Mashhad

Note

These lectures were built using the new Sphinx-based Jupyter Book 2.0 tool set, as part of the ExecutableBookProject. They are intended mainly as a demonstration of these tools. Instructions for how to build them from source can be found in the Jupyter Book documentation.

Introduction to NumPy

Data Manipulation with Pandas

Visualization with Matplotlib

Machine Learning

Further Machine Learning Resources#

  • The Scikit-Learn website: The Scikit-Learn website has an impressive breadth of documentation and examples covering some of the models discussed here, and much, much more. If you want a brief survey of the most important and often-used machine learning algorithms, this is a good place to start.

  • SciPy, PyCon, and PyData tutorial videos: Scikit-Learn and other machine learning topics are perennial favorites in the tutorial tracks of many Python-focused conference series, in particular the PyCon, SciPy, and PyData conferences. Most of these conferences publish videos of their keynotes, talks, and tutorials for free online, and you should be able to find these easily via a suitable web search (for example, “PyCon 2022 videos”).

  • Introduction to Machine Learning with Python, by Andreas C. Müller and Sarah Guido (O’Reilly). This book covers many of the machine learning fundamentals discussed in these chapters, but is particularly relevant for its coverage of more advanced features of Scikit-Learn, including additional estimators, model validation approaches, and pipelining.

  • Machine Learning with PyTorch and Scikit-Learn, by Sebastian Raschka (Packt). Sebastian Raschka’s most recent book starts with some of the fundamental topics covered in these chapters, but goes deeper and shows how those concepts apply to more sophisticated and computationally intensive deep learning and reinforcement learning models using the well-known PyTorch library.

I should mention that the original material was from Jake VanderPlas’s Python Data Science Handbook. I used his notebooks and modified it to suit my own needs and preferences. I would like to thank him for his great work and generosity.

Contents