# Python Machine Learning, 3rd Edition

Python Machine Learning, Third Edition is a practical guide to machine learning and deep learning for readers who know some Python. It combines conceptual explanations with code examples using scikit-learn and TensorFlow 2, covering topics from model evaluation and regression to neural networks, GANs, and reinforcement learning.

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## At a glance

- Uses Python examples to explain machine learning concepts alongside practical implementation.
- Covers scikit-learn and TensorFlow 2, including Keras API features.
- Topics include data preprocessing, classification, regression, clustering, model evaluation, and hyperparameter tuning.
- Deep-learning coverage includes neural networks, image classification, recurrent networks, GANs, and reinforcement learning.
- Also discusses sentiment analysis and embedding a machine-learning model in a web application.

## Pros

- Connects theory and hands-on code across a broad range of machine-learning topics.
- Includes practical coverage of both scikit-learn workflows and TensorFlow-based deep learning.
- The stated topic range extends from foundational techniques to GANs and reinforcement learning.

## Specifications

### Book details
- Edition: 3rd ed.
- Publisher: Packt Publishing
- Publication date: December 12, 2019
- Language: English
- Print length: 770 pages
- ISBN-10: 1789955750
- ISBN-13: 978-1789955750
### Dimensions and installation
- Weight listed: 2.87 pounds
- Dimensions: 7.5 x 1.74 x 9.25 inches

## Before you buy

- This third edition was published in 2019 and describes updates for TensorFlow 2 and then-current versions of NumPy, SciPy, and scikit-learn. Readers needing guidance for newer software releases should confirm that the examples match their current environment. Customer feedback includes a concern about print legibility.

## A good fit for

- Python developers beginning machine learning who want explanations as well as coding examples.
- Data scientists, developers, students, or researchers looking for a wide-ranging reference to traditional machine learning and deep learning.

## Less suited to

- Readers seeking a book that starts with teaching Python from scratch.
- Buyers who need coverage explicitly updated for software releases newer than those described in this edition.

## Customer feedback

Customer feedback describes the explanations and coding illustrations as understandable and says the book can be practically useful, with a reported balance between intuition and mathematical rigor. Some feedback criticizes legibility. For readers working through its code examples, the content praise supports its practical appeal, while the legibility concern matters for sustained reading. These reports are opinions and do not establish that every copy has the same print quality.
- Positive themes: Readers report clear explanations and coding illustrations.
- Positive themes: Feedback describes a useful balance of intuition and mathematical rigor, and practical relevance.
- Negative themes: A customer reports that the text is very difficult to read.

## Frequently asked questions

### Does the book cover both machine learning and deep learning?
Yes. The listed content spans conventional machine-learning methods and deep-learning topics such as neural networks, image classification, recurrent networks, GANs, and reinforcement learning.
### Which Python frameworks are covered?
The title and listing specify scikit-learn for machine learning and TensorFlow 2 for deep learning, including TensorFlow's Keras API.
### Is this a beginner Python programming book?
No. The listing is aimed at readers who already know some Python and want to learn machine learning or deepen existing knowledge.
### How current are the software examples?
The listing describes updates for TensorFlow 2 and current-at-publication versions of NumPy, SciPy, and scikit-learn. Because the edition was published in 2019, check whether its examples fit the versions you use.

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## Images

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