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# *Machine Learning with PyTorch and Scikit-Learn* Book
## Code Repository
Paperback: 770 pages
Publisher: Packt Publishing
Language: English
ISBN-10: 1801819319
ISBN-13: 978-1801819312
Kindle ASIN: B09NW48MR1
[<img src="./.other/cover_1.jpg" width="248">](https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-scikit-learn-ebook-dp-B09NW48MR1/dp/B09NW48MR1/)
## Links
- [Amazon link](https://www.amazon.com/Machine-Learning-PyTorch-Scikit-Learn-scikit-learn-ebook-dp-B09NW48MR1/dp/B09NW48MR1/)
- [Packt link](https://www.packtpub.com/product/machine-learning-with-pytorch-and-scikit-learn/9781801819312)
- [Blog post summarizing the contents](https://sebastianraschka.com/blog/2022/ml-pytorch-book.html)
## 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)**.
**In addition, Zbynek Bazanowski contributed [this helpful guide](supplementary/running-on-colab.pdf) explaining how to run the code examples on Google Colab.**
**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. Predicting Continuous Target Variables with Regression Analysis [[open dir](ch09)]
10. Working with Unlabeled Data – Clustering Analysis [[open dir](ch10)]
11. Implementing a Multi-layer Artificial Neural Network from Scratch [[open dir](ch11)]
12. Parallelizing Neural Network Training with PyTorch [[open dir](ch12)]
13. Going Deeper -- The Mechanics of PyTorch [[open dir](ch13)]
14. Classifying Images with Deep Convolutional Neural Networks [[open dir](ch14)]
15. Modeling Sequential Data Using Recurrent Neural Networks [[open dir](ch15)]
16. Transformers -- Improving Natural Language Processing with Attention Mechanisms [[open dir](ch16)]
17. Generative Adversarial Networks for Synthesizing New Data [[open dir](ch17)]
18. Graph Neural Networks for Capturing Dependencies in Graph Structured Data [[open dir](ch18)]
19. Reinforcement Learning for Decision Making in Complex Environments [[open dir](ch19)]
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Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili. *Machine Learning with PyTorch and Scikit-Learn*. Packt Publishing, 2022.
@book{mlbook2022,
address = {Birmingham, UK},
author = {Sebastian Raschka, and Yuxi (Hayden) Liu, and Vahid Mirjalili},
isbn = {978-1801819312},
publisher = {Packt Publishing},
title = {{Machine Learning with PyTorch and Scikit-Learn}},
year = {2022}
}
## Coding Environment
Please see the [ch01/README.md](ch01/README.md) file for setup recommendations.
## Translations into other Languages
- Serbian Translation: [Mašinsko učenje uz PyTorch i Scikit-Learn](https://knjige.kombib.rs/masinsko-ucenje-uz-pytorch-i-scikit-learn).
ISBN: 9788673105772