Machine Learning Refined: Foundations, Algorithms, and Applications
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Machine Learning Refined: Foundations, Algorithms, and Applications
With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products.
Machine Learning Refined: Foundations, Algorithms, and Applications
Nº de artículo: 175468337

Machine Learning Refined: Foundations, Algorithms, and Applications

90% of respondents would recommend this to a friend

Nº de artículo: 175468337

USD 94

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With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products.
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What Stands Out

Comprehensive Overview
Covers foundational concepts, diverse algorithms, and their applications, making it a vital resource for both beginners and experienced practitioners looking to deepen their understanding of machine learning.
Practical Insights
Includes real-world examples and case studies that bridge theory and practice, helping readers apply machine learning techniques effectively in various domains and industries.
Expert Authorship
Authored by leading experts in the field, assuring readers of high-quality content that reflects the latest advancements and best practices in machine learning.

Detalles de producto

Machine Learning Refined: Foundations, Algorithms, and Applications
Publisher Cambridge University Press
Publication date 9 Jan. 2020
Edition 2nd
Language English
Print length 594 pages
ISBN-10 1108480721
ISBN-13 978-1108480727
Item weight 1.36 kg
Dimensions 17.15 x 3.18 x 24.77 cm

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for professionals seeking a comprehensive understanding of machine learning concepts, algorithms, and practical applications.

  • Students

    Perfect for university students in computer science or data science programs wanting foundational knowledge in machine learning.

  • Industry Professionals

    Beneficial for business analysts or engineers looking to implement machine learning solutions in real-world scenarios.

Not Suitable For
  • Beginners

    Not suitable for those with no background in programming or data science, as it may be overwhelming.

DESCRIPCIÓN DEL PRODUCTO

Machine Learning Refined: Foundations, Algorithms, and Applications

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Preguntas y respuestas de los clientes

  • Pregunta: What type of examples are included in the book?

    Respuesta: Examples are included from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology.
  • Pregunta: What online resources are provided?

    Respuesta: A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online.
  • Pregunta: Who is the target audience for this book?

    Respuesta: This book is ideal for graduate courses on machine learning and for individual reference and self-study.

Higher Education Editorial Review

Machine Learning Refined Foundations, Algorithms, And Applications offers a comprehensive 2nd edition published by Cambridge University Press. With 594 pages of in-depth exploration, it delves into various machine learning algorithms and their practical applications. The book is well-structured, making complex concepts accessible to readers. It is printed in English and has received positive feedback for its clarity and thoroughness, making it an essential resource for both students and professionals interested in the field of machine learning.

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ventajas

  • Comprehensive coverage of algorithms and applications
  • Well-structured and easy to understand
  • Suitable for both students and professionals
  • Thorough exploration of machine learning concepts
  • High-quality publication from a reputable publisher

Contras

  • Some readers may find it quite dense to read.

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características y beneficios

  • Intuitive and rigorous approach to machine learning
  • Includes essential mathematical prerequisites
  • Practical applications emphasized
  • Over 300 color illustrations for technical concepts
  • Over 100 in-depth coding exercises in python
  • Ideal for graduate courses and individual self-study

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