January 15, 2020

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The “Python Machine Learning (3rd edition)” book code repository

repo name rasbt/python-machine-learning-book-3rd-edition
repo link https://github.com/rasbt/python-machine-learning-book-3rd-edition
language Jupyter Notebook
size (curr.) 164410 kB
stars (curr.) 854
created 2019-06-07
license MIT License

Python Machine Learning (3rd Ed.) Code Repository

Python 3.6 License

Code repositories for the 1st and 2nd edition are available at

Python Machine Learning, 3rd Ed.

to be published December 12th, 2019

Paperback: 770 pages
Publisher: Packt Publishing
Language: English

ISBN-10: 1789955750
ISBN-13: 978-1789955750
Kindle ASIN: B07VBLX2W7

Table of Contents and Code Notebooks

Helpful installation and setup instructions can be found in the README.md file of Chapter 1

Please note that these are just the code examples accompanying the book, which we uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.

  1. Machine Learning - Giving Computers the Ability to Learn from Data [open dir]
  2. Training Machine Learning Algorithms for Classification [open dir]
  3. A Tour of Machine Learning Classifiers Using Scikit-Learn [open dir]
  4. Building Good Training Sets – Data Pre-Processing [open dir]
  5. Compressing Data via Dimensionality Reduction [open dir]
  6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [open dir]
  7. Combining Different Models for Ensemble Learning [open dir]
  8. Applying Machine Learning to Sentiment Analysis [open dir]
  9. Embedding a Machine Learning Model into a Web Application [open dir]
  10. Predicting Continuous Target Variables with Regression Analysis [open dir]
  11. Working with Unlabeled Data – Clustering Analysis [open dir]
  12. Implementing a Multi-layer Artificial Neural Network from Scratch [open dir]
  13. Parallelizing Neural Network Training with TensorFlow [open dir]
  14. Going Deeper: The Mechanics of TensorFlow [open dir]
  15. Classifying Images with Deep Convolutional Neural Networks [open dir]
  16. Modeling Sequential Data Using Recurrent Neural Networks [open dir]
  17. Generative Adversarial Networks for Synthesizing New Data [open dir]
  18. Reinforcement Learning for Decision Making in Complex Environments [open dir]

Raschka, Sebastian, and Vahid Mirjalili. Python Machine Learning, 3rd Ed. Packt Publishing, 2019.

address = {Birmingham, UK},  
author = {Raschka, Sebastian and Mirjalili, Vahid},  
edition = {3},  
isbn = {978-1789955750},   
publisher = {Packt Publishing},  
title = {{Python Machine Learning, 3rd Ed.}},  
year = {2019}  
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