Machine Learning: An Applied Mathematics Introduction, (Paperback)
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Machine Learning: An Applied Mathematics Introduction, (Paperback)
Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics
Machine Learning: An Applied Mathematics Introduction, (Paperback)
商品＃: 43340608

Machine Learning: An Applied Mathematics Introduction, (Paperback)

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商品＃: 43340608

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Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics
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この商品の利点

Applied Focus
This book emphasizes practical applications of machine learning concepts, making it ideal for those looking to implement theory in real-world scenarios.
Mathematical Foundation
It uniquely combines machine learning with applied mathematics, ensuring readers build a strong theoretical understanding to enhance their analytical skills in various fields.
Comprehensive Coverage
Offers extensive coverage of key algorithms and techniques, enabling a thorough grasp of the subject that caters to both beginners and advanced learners.

製品詳細

Shop Machine Learning: An Applied Mathematics Introduction, (Paperback) online at a best price in 日本. 809078782
  • Covers essential mathematics behind topics like K Nearest Neighbours, Regression Methods, and Neural Networks
  • Includes real-world examples from various fields such as finance, economics, politics, biology, sociology, gambling, and business
  • Authored by Paul Wilmott, who brings three decades of experience in mathematics education to the subject
  • Accessible introduction for anyone interested in understanding the foundations of machine learning
  • Provides a practical approach to learning machine learning without overwhelming with complex details
  • Suitable for readers who want to grasp the core concepts without delving too deep into theoretical aspects
Book formatPaperback
Fiction/nonfictionNon-Fiction
GenreTextbooks
Publication dateMay, 2019
Pages242
SubgenreGeneral
Series titleNo Series
Edition1
PublisherPanda Ohana Publishing
Original languagesEnglish
LanguageEnglish
Edu focusMath & Counting
Educational levelGeneral
Is collectibleN
Binding typePerfect Binding
Recording time0 min
Retail packagingSingle Piece
Assembled product dimensions (l x w x h)6.14 x 0.51 x 9.21 in (15.6 x 1.3 x 23.4 cm)
Assembled product weight0.76 lb (340 grams)
Bisac subject headingMathematics

どんな人にお勧めですか？

Suitable For
  • Mathematics Students

    Ideal for those studying applied mathematics, providing a solid foundation for understanding machine learning concepts.

  • Data Science Beginners

    Perfect for newcomers to data science who need to grasp mathematical principles underpinning machine learning techniques.

  • Professionals in Tech

    Beneficial for tech professionals seeking to enhance their skills in machine learning through an applied mathematics lens.

Not Suitable For
  • Complete Beginners

    Not suitable for individuals with no prior knowledge of mathematics or machine learning concepts.

製品説明書

Machine Learning: An Applied Mathematics Introduction, (Paperback)

何か質問はありますか？ チャットでお問い合わせください

お客様の質問と回答

  • 質問: Who is the author of the book?

    回答: The author of the book is Paul Wilmott.
  • 質問: What fields are covered in the real-world examples?

    回答: The real-world examples cover finance, economics, politics, biology, sociology, gambling, and business.
  • 質問: Is the book suitable for beginners?

    回答: Yes, the book is an accessible introduction for anyone who wants to understand the foundations of machine learning.

Paul Wilmott All Books Editorial Review

Machine Learning: An Applied Mathematics Introduction (Paperback) is a comprehensive guide that delves into the mathematical foundations crucial for understanding machine learning concepts. This non-fiction textbook, published in May 2019, spans 242 pages, making it an excellent resource for learners at a general educational level. With a perfect binding format, it's designed for easy handling during study sessions. The book adeptly bridges the gap between mathematics and its application in machine learning, marking it as an essential read. Readers can expect a thorough exploration of math and counting essential for grasping machine learning algorithms and techniques.

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長所

  • Accessible for general educational levels
  • Covers essential mathematical concepts
  • Well-structured for easy comprehension
  • Perfect binding ensures durability
  • Lightweight and portable format

短所

  • May lack advanced topics for experienced learners

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特徴と利点

  • K Nearest Neighbours
  • K Means Clustering
  • Naive Bayes Classifier
  • Regression Methods
  • Support Vector Machines
  • Self-Organizing Maps
  • Decision Trees
  • Neural Networks
  • Reinforcement Learning

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