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Learning From Data Hardcover January 1, 2012
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Learning From Data Hardcover January 1, 2012
Learning From Data Hardcover January 1, 2012
Item #: 9155833

Learning From Data Hardcover January 1, 2012

Item #: 9155833

KGS 3603

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What Stands Out

Comprehensive Coverage
Covers essential data analysis techniques and machine learning concepts, providing a thorough understanding for both beginners and experienced practitioners.
Practical Applications
Includes real-world examples and case studies, enabling readers to apply theoretical knowledge effectively in various data-driven scenarios.
Expert Authorship
Written by industry experts, ensuring high-quality content that reflects current trends and methodologies in data science and analytics.

Product Details

Shop Learning From Data Hardcover January 1, 2012 online at a best price in Kyrgyzstan. 1600490069
Package Weight1.4 Pound

Who Should Buy?

Suitable For
  • Aspiring Data Scientists

    This product provides foundational knowledge necessary for individuals pursuing a career in data science.

  • Students in Academics

    Ideal for university students studying statistics, machine learning, or computer science courses needing theoretical understanding.

  • Professionals Seeking Knowledge

    Suitable for professionals looking to enhance their data analysis skills to improve decision-making in their fields.

Not Suitable For
  • Beginners in Data

    Individuals with no prior experience in statistics or programming may struggle with the concepts presented in this book.

  • Casual Learners

    Those looking for light, easy-to-read materials should seek alternatives as this book is dense and technical.

  • Non-Technical Professionals

    Workers in non-technical roles may find the content too complex and less applicable to their daily tasks.

Product Description

Learning From Data Hardcover January 1, 2012

About This Item

Are you looking to expand your knowledge in the field of data analysis? Look no further than the Learning From Data Hardcover book. This insightful and comprehensive book, published on January 1, 2012, is a must-have resource for anyone interested in learning about data analysis. Whether you are a beginner or an experienced professional, this book has something to offer. Written by renowned experts in the field, this hardcover book covers a wide range of topics related to data analysis.

From the basics of statistics to advanced machine learning techniques, this book provides a solid foundation for understanding and applying data analysis methods. The Learning From Data Hardcover book is not just another textbook. It is a practical guide that includes real-world examples and case studies, making it easier to grasp complex concepts and apply them in real-life scenarios. The authors have ensured that the content is accessible and easy to understand, even for those with limited background knowledge in the subject. With this book, you will learn how to analyze and interpret data, make informed decisions, and gain valuable insights.

Whether you are a student, a researcher, or a professional in the field, this book will be a valuable addition to your library. Don't miss out on the opportunity to enhance your data analysis skills. Get your hands on the Learning From Data Hardcover book today and take your understanding of data analysis to new heights. Don't wait any longer – start mastering the art of data analysis now!.

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Customer Questions & Answers

  • Question: What is the main focus of 'Learning From Data'?

    Answer: The main focus of 'Learning From Data' is to introduce foundational concepts in the field of machine learning and data analysis. The book elaborates on statistical learning theory, the risk minimization approach, and practical algorithms for model evaluation. It's designed to bridge the gap between theoretical concepts and real-world application, making it suitable for both students and professionals looking to enhance their data analytical skills.
  • Question: Who are the authors of 'Learning From Data'?

    Answer: 'Learning From Data' is authored by renowned experts in the field, including Yaser Abu-Mostafa, who is a professor at Caltech, and his collaborators. Their collective experience brings a wealth of knowledge and an authoritative voice to the subject matter, ensuring that readers gain insights from some of the best minds in machine learning.
  • Question: Is 'Learning From Data' suitable for beginners?

    Answer: Yes, 'Learning From Data' is suitable for beginners as it starts with basic concepts and gradually progresses to more complex ideas in machine learning. The book includes clear explanations and practical examples to help readers understand how to apply theoretical concepts to real-world data. Beginners can benefit from the hands-on approach and exercises included in the text.
  • Question: What types of learning does this book cover?

    Answer: 'Learning From Data' covers various types of learning, including supervised learning, unsupervised learning, and reinforcement learning. Each segment discusses algorithms and frameworks relevant to these learning types, demonstrating their applications in real scenarios. This comprehensive exploration allows readers to grasp diverse methodologies used in the field.
  • Question: Are there any supplementary materials available for this book?

    Answer: Yes, supplementary materials include online resources such as lecture notes, video lectures, and exercises that accompany 'Learning From Data'. These resources enhance the learning experience by providing interactive content that complements the book's theoretical framework, allowing readers to reinforce their understanding through practical applications.
  • Question: What audience is 'Learning From Data' intended for?

    Answer: 'Learning From Data' is primarily intended for students in upper-level undergraduate and graduate courses, data science professionals, and machine learning practitioners. It serves as a valuable resource for anyone who wants to deepen their understanding of machine learning techniques and apply them in real-life settings across various domains.
  • Question: How does this book approach the topic of overfitting?

    Answer: 'Learning From Data' addresses the critical topic of overfitting through detailed discussions on model complexity and generalization. The book offers various methods to identify and mitigate overfitting, including cross-validation techniques and regularization strategies. This is essential for readers to build robust models that perform well on new, unseen data.
  • Question: Can 'Learning From Data' be used for self-study?

    Answer: Absolutely! 'Learning From Data' is structured in a way that supports self-study, with clear explanations, illustrations, and exercises designed for independent learning. The practical examples provided in the book help readers to practice and solidify their understanding, making it an ideal choice for individuals looking to enhance their knowledge at their own pace.
  • Question: What are some real-world applications of concepts from 'Learning From Data'?

    Answer: Concepts from 'Learning From Data' are widely applied in various fields such as finance for credit scoring, healthcare for predictive diagnostics, and marketing for customer segmentation. By utilizing the algorithms and methodologies discussed in the book, professionals can make data-driven decisions that enhance business outcomes and contribute to innovation in their respective industries.
  • Question: Where can I buy 'Learning From Data' in Kyrgyzstan?

    Answer: You can buy 'Learning From Data' on Ubuy, a reliable e-commerce platform that offers a wide selection of books including academic and professional texts. Ubuy provides a user-friendly interface and various purchasing options, making it convenient for users in Kyrgyzstan to access this essential resource for learning about data and machine learning.

Intelligence & Semantics Editorial Review

This machine learning textbook is highly recommended by customers for its clear and concise explanations of theoretical concepts and mathematical foundations behind algorithms. However, it is noted that there is limited coverage of some popular learning models and software methods and that the approach is somewhat abstract. Solutions to the included problems are not readily available, making it harder for self-study. Nevertheless, the online resources associated with the book, including new chapters, a video lecture series, and solution sets, address these criticisms and are highly praised for their quality and accessibility. Even though the additional chapters are not in the printed book, the book itself is highly recommended for its rigorous foundation in machine learning, and the added online resources make it one of the best introductions to machine learning available.

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Pros

  • Clear and concise explanations of theoretical concepts and mathematical foundations
  • Highly recommended for a rigorous foundation in machine learning
  • Additional online resources, such as new chapters and video lecture series with solution sets, highly praised

Cons

  • Limited coverage of popular learning models and software methods

Product Price History

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KGS 3603

Order now and get it around Monday, June 29

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