An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
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An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning with relevant applications and tutorial on implementing analyses and methods in R, targeted at statisticians and non-statisticians alike.
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
منتج #: 32827607

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

منتج #: 32827607

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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning with relevant applications and tutorial on implementing analyses and methods in R, targeted at statisticians and non-statisticians alike.
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تفاصيل المنتج

Explore the 2nd edition of An Introduction to Statistical Learning: with Applications in R. Find it at Ubuy قطر. Enhance your statistical skills with this comprehensive guide.
  • Accessible overview of statistical learning for analyzing complex datasets
  • Covers important modeling and prediction techniques with real-world applications
  • Includes tutorials on implementing analyses and methods in R
  • Written for statisticians and non-statisticians interested in statistical learning
  • Second edition features new chapters on deep learning, survival analysis, and multiple testing
  • R code updated for compatibility
Publisher Springer
Publication date July 30, 2021
Edition Second Edition 2021
Language English
Print length 622 pages
ISBN-10 1071614177
ISBN-13 978-1071614174
Item Weight 2.63 pounds (1.19 kg)
Dimensions 6.5 x 1.25 x 9.5 inches (16.5 x 3.2 x 24.1 cm)
Part of series Springer Texts in Statistics

وصف المنتج

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

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أسئلة العملاء & الإجابات

  • سؤال: What topics are covered in 'An Introduction to Statistical Learning'?

    إجابه: The book covers a wide range of topics including linear regression, classification, resampling methods, shrinkage methods, tree-based methods, support vector machines, and more. Each chapter offers theoretical concepts followed by practical applications using R. This makes it ideal for students and professionals looking to enhance their statistical modeling skills, providing both foundational knowledge and advanced analytical techniques for real-world data. The application of R in examples ensures that readers gain hands-on experience with statistical learning.
  • سؤال: Who is the target audience for this book?

    إجابه: This book is aimed at data scientists, statisticians, and graduate students in statistics and related fields. It provides a comprehensive introduction to statistical learning, making it suitable for beginners to intermediate users who wish to deepen their understanding. By combining theoretical aspects with practical examples, it enhances the learning experience for educators and practitioners alike. Readers can utilize the text as a course textbook or a self-study guide to navigate the world of statistical learning.
  • سؤال: How does this book differ from other statistical learning texts?

    إجابه: Unlike many statistical learning texts that can be dense and overly complex, this book emphasizes clarity and practical application. It provides straightforward explanations of concepts, alongside practical examples and R code snippets. Such an approach makes statistical learning accessible and easier to comprehend, catering to both theoretical insights and practical application. This makes the book a go-to resource for those looking to bridge the gap between theory and real-world usage.
  • سؤال: Can I use R packages with the examples in the book?

    إجابه: Yes, the book is designed to work seamlessly with various R packages, which enhance the practical learning experience. Each chapter includes code examples that illustrate how to implement statistical techniques using popular R packages, such as 'caret', 'randomForest', and 'ggplot2'. This integration allows readers to directly apply what they've learned in R, making it ideal for practicing data analysis and visualization in a hands-on manner.
  • سؤال: Is there any supplementary material available for this book?

    إجابه: Yes, accompanying the second edition, there are various resources available online including datasets used in the examples, R code snippets, and additional exercises. These resources can typically be found on the publisher's website or related academic repositories. This supplementary material enriches the learning experience, enabling readers to further engage with the content and apply their knowledge to practical problems in statistical learning.
  • سؤال: What is the importance of statistical learning in data science?

    إجابه: Statistical learning is crucial for data science as it underpins many popular machine learning algorithms. It provides the methodologies for understanding and predicting patterns in data, which is essential for actionable insights. By mastering statistical learning techniques, data analysts and scientists can build predictive models, perform data-driven decision-making, and uncover trends that inform strategic business choices. This skill set is increasingly valuable in various industries, from healthcare to finance.
  • سؤال: Is this book suitable for self-study?

    إجابه: Absolutely! The book is structured to facilitate self-study, featuring clear explanations, illustrative examples, and exercises at the end of each chapter. Readers can learn at their own pace, utilizing the practices alongside explanations to develop their skills in statistical learning. It serves as a complete guide for individuals interested in independently enhancing their knowledge base and technical skills in the field, leveraging R for practical applications.
  • سؤال: What prior knowledge is needed to read this book?

    إجابه: While the book can be approached with a basic understanding of statistics and R, it is beneficial for readers to have a foundational knowledge of linear algebra and probability theory. This background allows readers to more effectively grasp the content and context of statistical learning techniques. However, the book also introduces concepts progressively, making it accessible for those willing to learn and engage with new material along the way.
  • سؤال: Are there case studies included in the book?

    إجابه: Yes, 'An Introduction to Statistical Learning' includes real-world case studies that illustrate the application of statistical methods to complex problems. These case studies help readers understand the relevance and effectiveness of statistical learning techniques in various fields. By examining these cases, readers can see how to implement theoretical concepts in practice, enhancing their ability to tackle real datasets and derive meaningful conclusions from their analyses.
  • سؤال: Where can I buy 'An Introduction to Statistical Learning: with Applications in R'?

    إجابه: You can purchase 'An Introduction to Statistical Learning: with Applications in R' on Ubuy. Ubuy offers access to a variety of textbooks including this one, providing a convenient platform to browse and order the title directly from the comfort of your home. With a user-friendly interface, Ubuy simplifies the process of acquiring educational materials for those looking to enhance their knowledge in statistical learning.

Intelligence & Semantics Editorial Review

The book is highly recommended for beginners who want to learn about machine learning. It provides a clear and concise introduction to statistical learning and covers various methods in depth. The explanations are remarkably simple, making complex ideas easy to understand. While the book primarily uses R for programming, the focus is on understanding the inner workings of AI algorithms rather than training predictive models. The book strikes a good balance between theory and practical application. However, some readers may find the small print font size and slightly smaller dimensions of the hardcopy disappointing. Overall, this is a valuable resource for both beginners and experienced data scientists interested in statistics, data, and AI.

مراجعات العملاء وتقييماتهم

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إيجابيات

  • Clear and concise introduction to statistical learning
  • Simple explanations of complex ideas
  • Covers a variety of methods in depth
  • Good balance between theory and practical application

سلبيات

  • Uses R for programming, which may not be preferred by some readers

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QAR 297

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المميزات والفوائد

  • Accessible overview of statistical learning
  • Relevant applications and tutorial on implementing analyses and methods in R
  • Targeted at statisticians and non-statisticians alike
  • New chapters on deep learning, survival analysis, and multiple testing
  • Expanded treatments of naïve Bayes and generalized linear models
  • R code updated for compatibility

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