# Hands-On Machine Learning with C++ 2nd Edition

Edition: Hands-On Machine Learning with C++ is a practical guide to building machine-learning and deep-learning models with C++ libraries, then exporting and deploying models, including on mobile platforms. It spans core algorithms, data processing, model selection, experiment tracking, and Android object detection. The listing says a print or Kindle purchase includes a PDF, with email sign-up and proof of purchase required for the digital extras.

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- Market: US
- Language: en-US
- Currency: USD
- Category: Books, Music & Hobbies > Books, Music & Movies > Books & Comics
- Price: 38.54 USD
- Previous price: 49.99 USD
- Discount: 23%
- Price checked: 2026-11-14T09:30:51.810Z
- Rating: 4.6 / 5
- Reviews: 29

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## At a glance

- Covers supervised and unsupervised learning, clustering, classification, anomaly detection, recommender systems, and ensemble learning.
- Examples use C++ libraries including the PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib.
- Includes topics in BERT-based sentiment analysis, transfer learning, MLflow experiment tracking, and Optuna hyperparameter selection.
- Explains exporting and importing models and deploying object detection for Android.

## Pros

- Connects algorithm concepts with implementation across several C++ libraries and practical datasets.
- The subject coverage extends from data preparation and model evaluation to mobile deployment.
- The listing claims a free PDF with print or Kindle purchase, subject to sign-up and proof-of-purchase requirements.

## Specifications

### Book details
- Edition: 2nd
- Publication date: January 24, 2025
- Publisher: Packt Publishing
- Language: English
- Print length: 512 pages
### Dimensions and installation
- Weight listed: 1.94 pounds
- Dimensions: 7.5 x 1.17 x 9.25 inches
### Identifiers
- ISBN-10: 1805120573
- ISBN-13: 978-1805120575

## Before you buy

- The book is intended for readers with working C++ knowledge. The publisher also recommends familiarity with linear algebra and basic calculus. The free PDF and Packt Reader access require email sign-up and proof of purchase.

## A good fit for

- C++ developers learning machine learning through practical algorithms and examples.
- Data analysts, data scientists, and ML developers interested in implementing models in production or on mobile and embedded platforms.

## Less suited to

- Readers seeking a guide that teaches C++ from scratch, since working knowledge of the language is expected.
- Readers who want a machine-learning text without mathematics prerequisites, as the publisher describes linear algebra and basic calculus as useful preparation.

## Customer feedback

The aggregate ratings lean positive, with most ratings in the five-star category and a smaller three-star share. This provides a broadly favorable but limited signal, not an explanation of what readers liked or disliked. It cannot establish how useful the C++ examples or deployment coverage are for a particular reader.

## Frequently asked questions

### What background does this book assume?
The listing recommends working knowledge of C++, and also identifies linear algebra and basic calculus as useful preparation.
### Which machine-learning topics are covered?
The contents include data processing, model evaluation and selection, clustering, anomaly detection, dimensionality reduction, classification, recommender systems, ensembles, image classification, and sentiment analysis.
### Does it cover deploying models to mobile devices?
Yes. The listing describes model export and import and a chapter on mobile deployment, including real-time object detection for Android using C++.
### Is a PDF included?
The listing says a print or Kindle purchase includes a free PDF eBook. It also states email sign-up and proof of purchase are required for access.

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## Images

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