1 / 2Hands-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.
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.
Reference price: $49.99
Price checked: Nov 14, 2026
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Pros and cons
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.
Technical specifications
9 specifications| Edition | 2nd |
|---|---|
| Publication date | January 24, 2025 |
| Publisher | Packt Publishing |
| Language | English |
| Print length | 512 pages |
| Weight listed | 1.94 pounds |
|---|---|
| Dimensions | 7.5 x 1.17 x 9.25 inches |
| ISBN-10 | 1805120573 |
|---|---|
| ISBN-13 | 978-1805120575 |
What to know before buying
- 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.
Who is it for?
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 reviews on Amazon
- 5 stars
76% 76% - 4 stars
14% 14% - 3 stars
10% 10% - 2 stars
0% 0% - 1 star
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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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