Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python
Artikelnr.: 51328160

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine

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Artikelnr.: 51328160

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Besondere Merkmale

Practical Implementation
Offers hands-on examples and real-world scenarios, enabling readers to effectively implement supervised and unsupervised machine learning algorithms using Python, enhancing learning outcomes.
Comprehensive Coverage
Covers multiple machine learning libraries and toolkits, providing a thorough understanding of scientific Python, making it suitable for both novices and experienced practitioners looking to deepen their knowledge.
User-Friendly Approach
Written in an accessible style, this guide simplifies complex concepts and ensures readers can grasp machine learning fundamentals, making it an excellent resource for learners at all levels.

Produktdetails

Discover the ultimate guide to implementing machine learning algorithms in Python with hands-on examples. Shop now at Ubuy Austria.
Publisher Packt Publishing
Publication date July 24, 2020
Language English
Print length 384 pages
ISBN-10 1838826041
ISBN-13 978-1838826048
Item Weight 1.45 pounds (660 grams)
Dimensions 7.5 x 0.87 x 9.25 inches (19.1 x 2.2 x 23.5 cm)
Country of OriginThis item will be imported from US
Date First AvailableApril 03, 2021
What is in the boxHands-On Machine Learning with... For more details, please check description/product details

Für wen ist das Produkt geeignet?

Suitable For
  • Beginner Data Scientists

    Ideal for those starting in data science who want practical guidance on machine learning with Python.

  • Intermediate Practitioners

    Great for users with basic knowledge looking to deepen their understanding of machine learning algorithms.

  • Academic Researchers

    Useful for researchers needing a solid resource for implementing machine learning techniques in their projects.

Not Suitable For
  • Advanced Experts

    Not suitable for highly experienced practitioners seeking advanced theoretical insights or breakthroughs in machine learning.

PRODUKTBESCHREIBUNG

Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits: A practical guide to implementing supervised and unsupervised machine learning algorithms in Python

Haben Sie eine Frage? Mit uns chatten

Kundenfragen und -antworten

  • Frage: What topics are covered in 'Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits'?

    Antworten: This comprehensive guide covers a wide range of topics including both supervised and unsupervised machine learning techniques. Readers will learn about essential algorithms, data preprocessing, model evaluation, and practical applications using scikit-learn. Additionally, it explains how to leverage Python toolkits for data visualization and handling larger datasets effectively. This book is ideal for data enthusiasts looking to enhance their skills through practical examples and case studies.
  • Frage: Who is the target audience for this machine learning guide?

    Antworten: The book is designed for students, professionals, and anyone interested in diving into machine learning. Whether you're a beginner wanting to grasp the basics or an experienced practitioner seeking to refine your skills with intuitive examples, this guide caters to all levels. Educators and data science mentors can also utilize it as a resource for teaching fundamental concepts in a structured and approachable way.
  • Frage: Is prior programming knowledge necessary to use this book?

    Antworten: While some programming background is beneficial, having a strong command of Python is not strictly necessary. The book provides foundational insights into Python as it applies to machine learning. Beginners may find it slightly challenging initially, but with the practical examples provided throughout the text, they can progressively build their knowledge in Python and machine learning concepts.
  • Frage: How are practical exercises integrated in the book?

    Antworten: The book emphasizes hands-on learning by incorporating practical exercises, coding examples, and projects that apply the concepts discussed in each chapter. Every section typically concludes with a set of problems that encourage readers to implement the learned techniques. These exercises enable readers to understand real-world applications, ensuring they can apply the theories effectively in practical scenarios, such as developing predictive models.
  • Frage: Are there case studies or real-world examples included?

    Antworten: Yes, the book includes several case studies and real-world examples that illustrate how machine learning algorithms can be implemented in various domains such as healthcare, finance, and marketing. These examples help contextualize theoretical knowledge and demonstrate the tangible impacts of machine learning solutions in solving complex problems.
  • Frage: Can I use this book as a reference for data science projects?

    Antworten: Absolutely! This book serves as an excellent reference guide for data science projects. With its emphasis on practical applications and the Python toolkits, you can easily draw inspiration for your own projects. Whether you're working on personal, academic, or professional data science challenges, the techniques and insights provided will enhance your project outcomes significantly.
  • Frage: What programming libraries are primarily explored in this book?

    Antworten: The primary focus is on scikit-learn, a robust library tailored for machine learning implementations in Python. Additionally, the book explores libraries like NumPy and pandas for data manipulation, as well as Matplotlib and Seaborn for data visualization. By covering these libraries, the book equips readers with the necessary tools to effectively conduct data analysis and build predictive models.
  • Frage: Is there a digital version available for this book?

    Antworten: Yes, a digital version of 'Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits' is available for readers who prefer an electronic format. This version allows for easy navigation, searchability, and the convenience of accessing the content on multiple devices. It’s an excellent option for on-the-go learning and reference.
  • Frage: What are the prerequisites for understanding the content of this book?

    Antworten: A basic understanding of programming concepts, particularly in Python, is recommended to fully grasp the content. Familiarity with fundamental statistics and mathematics, especially concepts like linear algebra and probability, will also be beneficial. This foundational knowledge will enable readers to better engage with the material and effectively apply machine learning techniques presented in the book.
  • Frage: Where can I buy Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits in Austria?

    Antworten: You can purchase 'Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits' from Ubuy, a reliable e-commerce platform offering a wide selection of books and learning materials. Ubuy provides a seamless shopping experience, allowing you to easily find and acquire this essential resource for machine learning directly from your location.

Machine Theory Editorial Review

**Editorial Review** "Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits" serves as a commendable introduction to machine learning for aspiring data scientists and any curious learner eager to explore the intricacies of developing machine learning algorithms. The book effectively demystifies complex concepts by presenting them in an accessible manner, making it particularly beneficial for readers without a strong mathematics background. It details the implementation of various algorithms using Python and the widely-used scikit-learn library, providing the reader with real-life applications and code examples that can be leveraged for building more significant applications. The content is practically oriented, covering both supervised and unsupervised learning, with a focus on how to analyze and interpret imperfect data. Readers are guided through standard algorithms, such as decision trees, KNN classification, and Naive Bayes, implemented with familiar datasets like the Iris dataset and Boston housing prices. The author’s intent to empower readers with concrete knowledge is evident as they progress toward developing independent machine learning applications. However, there are criticisms regarding the organization and depth of content. Certain foundational topics were introduced post-algorithm discussions, which may hinder the flow for some learners. While the book does provide a fair overview of key concepts, it cannot be relied upon as an exhaustive resource to completely master the subject; more advanced learners or those looking for comprehensive theoretical insights may find it lacking. The absence of a glossary is also noted as a potential inconvenience for quick reference to important terms. Ultimately, this book is recommended for beginners and serves as a stepping stone in the journey to more advanced machine learning studies. While it has its limitations, it offers a solid foundation for those looking to delve into machine learning with Python and scikit-learn. **Pros and Cons** **

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Vorteile

  • Easy-to-understand introduction to machine learning concepts.
  • Practical implementation focus using the scikit-learn library.
  • Numerous code examples and real-life applications for hands-on learning.
  • Suitable for readers without a strong math or programming background.

Nachteile

  • Lacks depth in theoretical discussions and coverage of certain algorithms.

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