padamshrestha / python-machine-learning-book-3rd-edition

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

Python Machine Learning (3rd Ed.) Code Repository

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

Links

• Amazon Page
• Packt Page

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.

@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}  
}