Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition Paperback – 12 Dec. 2019
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Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition Paperback – 12 Dec. 2019
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. 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.
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition Paperback – 12 Dec. 2019
Artikelnr.: 76232075

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition Paperback – 12 Dec. 2019

Artikelnr.: 76232075

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Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. 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.
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Comprehensive Coverage
This edition provides an extensive overview of both machine learning and deep learning, making it suitable for beginners and advanced users alike, ensuring a rounded understanding of Python frameworks.
Hands-On Examples
Includes practical examples using scikit-learn and TensorFlow 2, allowing readers to apply concepts directly in coding scenarios, enhancing learning and retention through real-world applications.
Updated Content
As a 3rd edition, it incorporates the latest advancements in machine learning and deep learning methodologies, ensuring readers are equipped with contemporary insights and techniques in the evolving tech landscape.

Produktdetails

Explore the power of Python for Machine Learning and Deep Learning with scikit-learn and TensorFlow. Discover the latest techniques in this 3rd edition book. Shop at Ubuy Liechtenstein
  • Updated 3rd edition of Python Machine Learning book
  • Covers machine learning and deep learning with Python, scikit-learn, and TensorFlow 2
  • Includes updated chapters on NumPy, SciPy, and scikit-learn
  • New chapters on Generative Adversarial Networks (GANs) and reinforcement learning
  • Emphasizes practical code examples and hands-on learning
  • Introduces PyTorch and new topics like transformers, gradient boosting, and graph neural networks
Artikelgewicht680 Gramm

Für wen ist das Produkt geeignet?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners looking to understand machine learning fundamentals and build a solid foundation in Python.

  • Software Developers

    Great for developers wanting to integrate machine learning capabilities into existing applications using Python libraries.

  • Students and Educators

    Suitable for academic purposes, providing clear explanations and practical examples for coursework or research.

Not Suitable For
  • Complete Beginners

    Not suitable for those with no programming background, as some prior knowledge of Python is recommended.

  • Experts in ML

    Experienced practitioners may find basic concepts and examples insufficient for advanced machine learning applications.

  • Non-Technical Audiences

    Individuals without technical knowledge or interest in machine learning may struggle to understand the material presented.

PRODUKTBESCHREIBUNG

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition Paperback – 12 Dec. 2019

Haben Sie eine Frage? Mit uns chatten

Kunden Fragen und Antworten

  • Frage: Who is the target audience for Python Machine Learning book?

    Antworten: The book is targeted towards developers and data scientists who want to create practical machine learning and deep learning code.
  • Frage: What does the book cover?

    Antworten: The book covers all the essential machine learning techniques in depth, and introduces readers to TensorFlow 2.0, latest additions to scikit-learn and cutting-edge reinforcement learning techniques based on deep learning.
  • Frage: What can I learn from this book?

    Antworten: You would be able to master the frameworks, models, and techniques that enable machines to 'learn' from data, apply machine learning to image classification, sentiment analysis, intelligent web applications, and more.

AI & Machine Learning Editorial Review

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition is a comprehensive book that is best suited for junior data scientists looking to refresh their knowledge or for individuals with technical backgrounds (such as computer science or mathematics) who are new to machine learning. The book offers technical detail and provides explained code examples for further study. However, some readers have encountered difficulties in setting up the required environment to run the code. The explanations provided in the book were not deemed helpful by one reviewer who struggled to get the code running, despite having experience with different Python versions. It is important to note that the book's code relies on specific packages and versions, which are required for correct execution. For Python 3.9.13, the recommended package versions are as follows: NumPy 1.21.2, SciPy 1.7.0, Scikit-learn 1.0, Matplotlib 3.4.3, and pandas 1.3.2. The book's physical quality receives mixed reviews. While some readers criticize the thin and cheap paper, others highlight the lack of color in the charts and graphs presented in the book, which is a disadvantage Considering the importance of visual elements in understanding machine learning concepts. In summary, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition is a valuable resource for junior data scientists and individuals with technical backgrounds who are new to machine learning. However, readers should be aware of potential difficulties in setting up the required environment to run the code and the lack of color in the printed version.

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Vorteile

  • Provides technical detail and explained code examples.
  • Suitable for junior data scientists and individuals with technical backgrounds.
  • Covers machine learning and deep learning with Python, scikit-learn, and TensorFlow 2.

Nachteile

  • Difficulty in setting up the required environment to run the code.

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CHF 110

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Merkmale und Vorteile

  • Comprehensive guide to machine learning and deep learning with Python
  • Covers all the essential machine learning techniques in depth
  • Introduces readers to TensorFlow 2.0 and latest additions to scikit-learn
  • Explores cutting-edge reinforcement learning techniques based on deep learning
  • Ideal for developers and data scientists who want to create practical machine learning and deep learning code
  • Teaches principles behind machine learning, allowing you to build models and applications for yourself