## Python Machine Learning (3rd Ed.) Code Repository
[](#)
[](LICENSE.txt)
Code repositories for the 1st and 2nd edition are available at
- https://github.com/rasbt/python-machine-learning-book and
- https://github.com/rasbt/python-machine-learning-book-2nd-edition
**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
[<img src="./.other/cover_1.jpg" width="248">](https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1789955750/)
## Links
- [Amazon Page](https://www.amazon.com/Python-Machine-Learning-scikit-learn-TensorFlow/dp/1789955750/)
- [Packt Page](https://www.packtpub.com/data/python-machine-learning-third-edition)
## Table of Contents and Code Notebooks
**Helpful installation and setup instructions can be found in the [README.md file of Chapter 1](ch01/README.md)**
**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](ch01)]
2. Training Machine Learning Algorithms for Classification [[open dir](ch02)]
3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[open dir](ch03)]
4. Building Good Training Sets – Data Pre-Processing [[open dir](ch04)]
5. Compressing Data via Dimensionality Reduction [[open dir](ch05)]
6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[open dir](ch06)]
7. Combining Different Models for Ensemble Learning [[open dir](ch07)]
8. Applying Machine Learning to Sentiment Analysis [[open dir](ch08)]
9. Embedding a Machine Learning Model into a Web Application [[open dir](ch09)]
10. Predicting Continuous Target Variables with Regression Analysis [[open dir](ch10)]
11. Working with Unlabeled Data – Clustering Analysis [[open dir](ch11)]
12. Implementing a Multi-layer Artificial Neural Network from Scratch [[open dir](ch12)]
13. Parallelizing Neural Network Training with TensorFlow [[open dir](ch13)]
14. Going Deeper: The Mechanics of TensorFlow [[open dir](ch14)]
15. Classifying Images with Deep Convolutional Neural Networks [[open dir](ch15)]
16. Modeling Sequential Data Using Recurrent Neural Networks [[open dir](ch16)]
17. Generative Adversarial Networks for Synthesizing New Data [[open dir](ch17)]
18. Reinforcement Learning for Decision Making in Complex Environments [[open dir](ch18)]
---
<br>
<br>
Raschka, Sebastian, and Vahid Mirjalili. *Python Machine Learning, 3rd Ed*. Packt Publishing, 2019.
@book{RaschkaMirjalili2019,
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}
}