# Python Machine Learning By Example, 4th Edition

Python Machine Learning By Example is a fourth-edition book by Yuxi \(Hayden\) Liu that teaches machine learning through Python projects, from classical methods to transformers and multimodal models. It covers both implementation from scratch and work with libraries including PyTorch, TensorFlow, scikit-learn, and pandas, so it suits readers who can already program in Python and want applied ML coverage.

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- Rating: 4.9 / 5
- Reviews: 84

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## At a glance

- Covers classical machine learning, neural networks, computer vision, recurrent networks, transformers, and multimodal models.
- Projects include recommendation engines, ad-click prediction, stock-price prediction, image classification, and image search.
- Includes guidance on data preparation, model development, regularization, and other machine learning practices.
- Book description says readers receive access to a PDF copy, AI assistant, and next-generation reader. Check the selected edition's listing for access terms.

## Pros

- The range of projects connects algorithm topics to concrete tasks, including text analysis, image recognition, and prediction.
- Coverage spans both implementations from scratch and frameworks such as PyTorch and TensorFlow.
- A dedicated best-practices chapter complements coverage of topics from classical algorithms through transformers.

## Cons

- The intended reader is expected to have Python programming knowledge, so this is not presented as a Python-from-zero textbook.

## Specifications

### Publication details
- Edition: 4th
- Publisher: Packt Publishing
- Publication date: July 31, 2024
- Language: English
- Print length: 518 pages
- ISBN-10: 1835085628
- ISBN-13: 978-1835085622
### Dimensions and installation
- Weight listed: 2.12 pounds
- Dimensions: 7.5 x 1.19 x 9.25 inches

## A good fit for

- Python programmers moving into practical machine learning projects.
- Data scientists, analysts, ML engineers, and students seeking coverage from classical methods to transformers and multimodal modeling.
- Readers interested in hands-on examples using PyTorch, TensorFlow, scikit-learn, and pandas.

## Less suited to

- Readers who need an introduction to Python programming before starting machine learning.
- Buyers seeking a narrowly focused manual on only one framework or one machine learning topic.

## Customer feedback

Aggregate customer ratings are strongly concentrated in the five-star category, with a small share spread across lower ratings. That is a generally favorable rating pattern, but no review comments or reasons for the ratings are provided, so it cannot show which chapters readers liked or how useful the projects were in practice.

## Frequently asked questions

### What background does this book expect?
The listing describes it as suitable for readers with Python programming knowledge. It is aimed at beginners to machine learning as well as experienced practitioners, rather than Python beginners.
### What topics does the fourth edition cover?
It includes classical algorithms, neural networks, computer vision, sequence modeling, NLP transformers such as BERT and GPT, and multimodal computer vision models.
### Are projects included?
The described examples include a movie recommendation engine, online ad-click prediction, stock-price prediction, image classification, and an image search engine.
### Which software libraries are discussed?
The listing names PyTorch, TensorFlow, scikit-learn, and pandas.
### Does it include digital extras?
The description claims a PDF copy, AI assistant, and next-generation reader with the book. The terms and availability of those extras are not specified here, so confirm them in the selected listing.

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## Images

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