---
product_id: 391326528
title: "Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python"
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---

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

**Price:** 22351 РСД
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- **What is this?** Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
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## Description

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch's simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book Description Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Explore frameworks, models, and techniques for machines to 'learn' from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra. Table of Contents Giving Computers the Ability to Learn from Data Training Simple Machine Learning Algorithms for Classification A Tour of Machine Learning Classifiers Using Scikit-Learn Building Good Training Datasets – Data Preprocessing Compressing Data via Dimensionality Reduction Learning Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying Machine Learning to Sentiment Analysis Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data – Clustering Analysis Implementing a Multilayer Artificial Neural Network from Scratch (N.B. Please use the Look Inside option to see further chapters)

Review: Well-Structured and Practical - This is an excellent hands-on book for learning modern machine learning with Python. I especially like how it combines both scikit-learn and PyTorch, covering everything from ML fundamentals to deep learning in a clear, structured way. The explanations are easy to follow, the code examples are practical, and the content is well organized. Whether you're a student, practitioner, or looking to refresh your knowledge, this book is a great resource to keep on your shelf. Highly recommended!
Review: Excellent book on ML - This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #33,962 in Books ( See Top 100 in Books ) #1 in Speech & Audio Processing #13 in Computer Neural Networks #16 in Natural Language Processing (Books) |
| Customer Reviews | 4.6 out of 5 stars 526 Reviews |

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## Customer Reviews

### ⭐⭐⭐⭐⭐ Well-Structured and Practical
*by A***R on July 27, 2026*

This is an excellent hands-on book for learning modern machine learning with Python. I especially like how it combines both scikit-learn and PyTorch, covering everything from ML fundamentals to deep learning in a clear, structured way. The explanations are easy to follow, the code examples are practical, and the content is well organized. Whether you're a student, practitioner, or looking to refresh your knowledge, this book is a great resource to keep on your shelf. Highly recommended!

### ⭐⭐⭐⭐⭐ Excellent book on ML
*by P***R on December 20, 2024*

This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.

### ⭐⭐⭐⭐ Just learning it
*by A***R on December 10, 2025*

Nice learning book just have to finish it

## 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 Serbia*
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*Last updated: 2026-09-03*