---
product_id: 394716829
title: "Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python"
price: "KSh21181"
currency: KES
in_stock: true
reviews_count: 11
url: https://www.desertcart.co.ke/products/394716829-machine-learning-with-pytorch-and-scikit-learn-develop-machine-learning
store_origin: KE
region: Kenya
---

# Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

**Price:** KSh21181
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- **What is this?** Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
- **How much does it cost?** KSh21181 with free shipping
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## Description

Buy Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python by Sebastian Raschka, Yuxi (Hayden) Liu, Vahid Mirjalili (ISBN: 9781801819312) from desertcart's Book Store. Everyday low prices and free delivery on eligible orders.

Review: This book could make PyTorch mainstream - I have used Sebastian Raschka's books in my teaching at the University Of Oxford before As usual, this book is excellent in its technical detail and thoroughness. However, it could also help to make PyTorch more mainstream. PyTorch has been gaining traction, but still mostly in the academic / research community. PyTorch has some excellent libraries (such as fast.ai) but still the world of PyTorch is a bit away from traditional Python for ML But by taking an approach of Scikit-Learn and PyTorch, this book could introduce PyTorch to a larger/mainstream audience of SKLearn users using a familiar paradigm. On first impressions, technically, the book is very much an enhancement of the previous book from Sebastian also (ex now includes transformers and GANs). Finally, I am also interested in PyTorch from the perspective of the metaverse. So, all in all an excellent - must read book - another great reference book from the author
Review: The right level of detail - Very up-to-date - By no means an absolute beginner book. It strikes the correct balance: all the details to grasp the main concept, and a lot of pointers to more detailed explanations (often the very papers introducing the technology). In my case, as an experienced developer, with ML in my university curriculum (from two decades ago) a real trove of material. Most recent technology as transformers (e.g. GPT) are covered, as are the best python libraries from PyTorch to huggingface. The correct amount of code, enough to understand the concept, is found in the book. And the code I tried always worked correctly. A must have book!

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | 225,520 in Books ( See Top 100 in Books ) 206 in Higher Mathematical Education 217 in Computer Information Systems 1,180 in Computing & Internet Programming |
| Customer reviews | 4.6 4.6 out of 5 stars (469) |
| Dimensions  | 19.05 x 4.45 x 23.5 cm |
| ISBN-10  | 1801819319 |
| ISBN-13  | 978-1801819312 |
| Item weight  | 1.41 kg |
| Language  | English |
| Print length  | 770 pages |
| Publication date  | 25 Feb. 2022 |
| Publisher  | Packt Publishing |

## Images

![Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python - Image 1](https://m.media-amazon.com/images/I/615IIVepfjL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ This book could make PyTorch mainstream
*by A***R on 25 February 2022*

I have used Sebastian Raschka's books in my teaching at the University Of Oxford before As usual, this book is excellent in its technical detail and thoroughness. However, it could also help to make PyTorch more mainstream. PyTorch has been gaining traction, but still mostly in the academic / research community. PyTorch has some excellent libraries (such as fast.ai) but still the world of PyTorch is a bit away from traditional Python for ML But by taking an approach of Scikit-Learn and PyTorch, this book could introduce PyTorch to a larger/mainstream audience of SKLearn users using a familiar paradigm. On first impressions, technically, the book is very much an enhancement of the previous book from Sebastian also (ex now includes transformers and GANs). Finally, I am also interested in PyTorch from the perspective of the metaverse. So, all in all an excellent - must read book - another great reference book from the author

### ⭐⭐⭐⭐⭐ The right level of detail - Very up-to-date
*by K***R on 13 January 2023*

By no means an absolute beginner book. It strikes the correct balance: all the details to grasp the main concept, and a lot of pointers to more detailed explanations (often the very papers introducing the technology). In my case, as an experienced developer, with ML in my university curriculum (from two decades ago) a real trove of material. Most recent technology as transformers (e.g. GPT) are covered, as are the best python libraries from PyTorch to huggingface. The correct amount of code, enough to understand the concept, is found in the book. And the code I tried always worked correctly. A must have book!

### ⭐⭐⭐⭐⭐ In-depth book covering numerous topics. Highly recommended.
*by D***T on 27 March 2022*

This book is a comprehensive, wide-ranging detailed, book that covers a huge range of different topic areas in great detail. Make no mistake this book is big - 700+ pages of in-depth content, and much, nay, most of it not aimed at the casual beginner. Across 19 chapters the authors go through topic such as building good training sets, dimension reduction techniques, best practices, ensembles, PyTorch, Scikit-Learn, CNNs, RNNs, transformers, GANs and much much much more. I, personally, have not read any other books by these authors, but I have bought and read many Packt books previously so I knew potentially what I would be getting. This book is so big and in-depth that there is no no way go through and digest, understand and remember everything in one go. And there is little point trying to remember everything anyway, it's much better to use the book as a reference book. As with other Packt books this book isn't just all about reading, there are plenty of coding samples that one can follow as progressing through the book. There is a handy summary at the end of chapter but no test yourself exercises, which may have been useful but would have made a big book even bigger. Giving my own interests I must make a special mention for chapter 16, the chapter on Transformers and NLP. The chapter is really well-written and explains the concepts of attention and how it is used in transformers, introduces Bert and also describes how to fine-tune it. All useful knowledge. In summary I have absolutely no hesitation in recommending this book. The authors have done a fantastic job and there is something for everyone in here. Highly recommended and a must buy.

## Frequently Bought Together

- Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Build a Large Language Model (From Scratch)

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*Product available on Desertcart Kenya*
*Store origin: KE*
*Last updated: 2026-03-04*