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rasbt/python-machine-learning-book-2nd-edition

★ 7,201Jupyter NotebookAudience · researcherComplexity · 2/5Setup · moderate

TLDR

This repo is the companion code for the book "Python Machine Learning" second edition, containing 16 Jupyter Notebook chapters on machine learning and deep learning using scikit-learn and TensorFlow.

Mindmap

mindmap
  root((repo))
    What it does
      Book companion code
      16 chapter notebooks
    Topics covered
      Classification
      Neural networks
      Image recognition
      Sequence modeling
    Tools used
      scikit-learn
      TensorFlow
      Jupyter Notebooks
    Audience
      Book readers
      ML learners
    Notes
      Needs the book text
      Third edition exists
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Code map

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Things people build with this

USE CASE 1

Follow along with the Python Machine Learning second edition book chapter by chapter using runnable code notebooks.

USE CASE 2

Study how to build and train neural networks with TensorFlow through the worked examples in the deep learning chapters.

USE CASE 3

Practice classification, image recognition, and sequence modeling using the scikit-learn examples in the notebooks.

Tech stack

PythonJupyter Notebookscikit-learnTensorFlow

Getting it running

Difficulty · moderate Time to first run · 30min

Notebooks are designed to be read alongside the physical book and may not make sense as standalone resources.

No license information was mentioned in the explanation.

In plain English

This repository contains the code that accompanies the second edition of the book "Python Machine Learning" by Sebastian Raschka and Vahid Mirjalili, published by Packt in September 2017. The book is a 622-page guide to building machine learning models using Python. The repository is a companion resource, not a standalone learning tool, the author notes that the notebooks may not be useful without the explanatory text and formulas from the book itself. The code is organized into 16 chapters, each stored as a Jupyter Notebook. Jupyter Notebooks are interactive documents that mix code, explanations, and output side by side, which makes them well-suited for educational material. The chapters cover a wide range of topics, starting with basic classification algorithms and working up through neural networks, image recognition, and sequence modeling. There is also a chapter on embedding a machine learning model into a web application. The tools used throughout the book include scikit-learn, a popular Python library for standard machine learning tasks, and TensorFlow, a framework from Google for building and training neural networks. The second edition was a significant update from the first: the deep learning chapters were reworked to use TensorFlow instead of the older Theano library, three new chapters on deep learning were added, and the figures and plots were redesigned. The author also addressed reader feedback about unclear explanations and corrected errors from the first edition. A third edition was published in December 2019, and the author links to that repository from this one. This second-edition repository remains available for readers of that version. Translations in German and Japanese were also published. Installation instructions for the required software are included in the first chapter's folder. The notebooks are intended to be worked through in order, following the book.

Copy-paste prompts

Prompt 1
Show me how to set up the Python environment for the Python Machine Learning 2nd edition notebooks and run the first chapter.
Prompt 2
Walk me through the neural network chapter in this repo, how does it train a model with TensorFlow step by step?
Prompt 3
Help me understand the image recognition chapter code, what dataset does it use and how do I run the notebook?
Prompt 4
How do I adapt the web app chapter code to deploy my own scikit-learn model as a web application?
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