# python-machine-learning-book-3rd-edition

**Repository Path**: albberk/python-machine-learning-book-3rd-edition

## Basic Information

- **Project Name**: python-machine-learning-book-3rd-edition
- **Description**: The "Python Machine Learning (3rd edition)" book code repository
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 2
- **Created**: 2024-09-09
- **Last Updated**: 2024-09-09

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Python Machine Learning (3rd Ed.) Code Repository

[![Python 3.6](https://img.shields.io/badge/Python-3.7-blue.svg)](#)
[![License](https://img.shields.io/badge/Code%20License-MIT-blue.svg)](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}  
    }