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

Learning From Data Hardcover January 1, 2012

Item #: 9155833

ZAR 804

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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 South Africa. 1600490069
  • Complete introduction to Machine Learning, with freely accessible online material
  • Designed for a short course on machine learning, covering core topics
  • Includes free access to online e-Chapters updated with current trends in Machine Learning
  • Balances theoretical and practical aspects, as well as mathematical and heuristic approaches
  • Authored by professors at California Institute of Technology, Rensselaer Polytechnic Institute, and National Taiwan University
  • Authors have extensive experience in teaching, consulting, and leading winning teams in machine learning
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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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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