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Python Machine Learning (2nd Edition)

Python Machine Learning (2nd Edition) is a public code repository accompanying the second edition of the book, with chapter-organized Jupyter notebooks and Python scripts for machine-learning and data-science examples. Its materials range from classification and data preprocessing to TensorFlow-based neural-network topics.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Python Machine Learning (2nd Edition) — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use the repository when studying the second edition and you need its chapter code alongside the book’s explanations. Chapter 1’s README is the stated place to find installation and setup instructions.

Who it is for

  • Readers following the second edition who want to inspect or work through its chapter code.
  • Learners seeking examples of Python machine-learning workflows, from classification and model evaluation to deep-learning applications.

Documented uses

  • Browse chapter-specific machine-learning examples.
  • Study notebook workflows for preprocessing, evaluation, regression, clustering and neural networks.
  • Use accompanying scripts or notebooks as a starting point for lessons and exercises.
Before you choose

Requirements & limits

The repository is public and displays an MIT code license. The README identifies the second edition as published in English and points to a separate code repository for the third edition.

  • The code is supplementary: the README warns that notebooks may not be useful without the book’s formulas and descriptive text.
  • The notebooks include figures and section headings, while chapter directories also contain Python scripts created from the notebooks.
  • The README directs readers seeking the third edition’s code to a separate repository.
  • The repository identifies Python 3.6 in a badge; Chapter 1’s README provides setup instructions.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Find a chapter notebook or script

Open the code materials associated with a chapter of the second edition.

Before you start

  • Access to the public GitHub repository.
  • The book’s explanatory text is useful context; the repository cautions that code examples may not be useful without it.

Steps

  1. Open the repository’s README and locate the chapter in its table of contents.

  2. Follow the chapter’s directory link to browse its code, or open its listed Jupyter notebook on GitHub.

  3. Use the chapter directory’s Python script when you want the script version of the notebook.

Expected outcome

You can view the selected chapter’s notebook or browse its accompanying code files.

Guide limitations

  • The examples accompany the book and may not be useful without its formulas and descriptive text.
  • The README recommends working with the notebook in a computing environment for stepwise execution; setup instructions are in Chapter 1’s README.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
rasbt/python-machine-learning-book-2nd-edition · Official homepage title · retained in its source language
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