Machine Learning Algorithms in Depth, (Paperback)
Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.
Machine Learning Algorithms in Depth, (Paperback)
Numéro d'article: 157394131

Machine Learning Algorithms in Depth, (Paperback)

Numéro d'article: 157394131

€ 73

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Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.
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Ce qui se démarque

Comprehensive Coverage
Delve into a wide array of machine learning algorithms, offering detailed explanations and real-world applications, appealing to both beginners and seasoned practitioners seeking deeper insights into the subject.
Practical Examples
Includes hands-on examples and case studies that reinforce learning, enabling readers to apply theoretical concepts in practice, thus enhancing both understanding and skill development in machine learning.
Expert Insights
Authored by industry leaders, this book provides valuable perspectives and best practices from experts, ensuring readers stay at the forefront of machine learning advancements and trends.

Détails du produit

Shop Machine Learning Algorithms in Depth, (Paperback) online at a best price in Belgium. 2397340033
Book formatPaperback
Fiction/nonfictionNon-Fiction
GenreComputing & Internet
Publication dateJune, 2024
Pages328
Reading levelProfessional & Vocational
SubgenreData Science
Series titleNo Series
Edition1
PublisherManning Publications
Original languagesEnglish
LanguageEnglish
Educational levelGeneral
Is collectibleN
Digital file formatPDF, Kindle, ePub
Digital reader formatPDF, Kindle, ePub
Digital audio file formatPDF, Kindle, ePub
Recording time0 min
Retail packagingSingle Piece
Assembled product dimensions (l x w x h)7.40 x 0.67 x 9.24 in (18.8 x 1.7 x 23.5 cm)
Assembled product weight1.25 lb (570 grams)
Bisac subject headingComputers

À qui est-ce destiné ?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners seeking a comprehensive understanding of machine learning concepts and practical applications.

  • Intermediary Practitioners

    A great resource for those with some experience looking to deepen their knowledge and refine their skills.

  • Educators and Students

    Useful for teaching materials and students seeking structured learning about machine learning algorithms in a textbook format.

Not Suitable For
  • Advanced Experts

    Not suitable for seasoned professionals who require cutting-edge research and advanced concepts beyond basic algorithms.

DESCRIPTION DU PRODUIT

Machine Learning Algorithms in Depth, (Paperback)

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Questions et réponses des clients

  • question: What prior knowledge do I need to read this book?

    répondre: Basic familiarity with linear algebra, probability, and calculus is recommended.
  • question: Does the book include practical coding examples?

    répondre: Yes, each algorithm is followed by hands-on Python implementations with code annotations.
  • question: Is there a digital version available with the print book?

    répondre: Yes, the purchase of the print book includes a free eBook in PDF and ePub formats.

Vadim Smolyakov All Books Editorial Review

Machine Learning Algorithms in Depth, a non-fiction paperback by Manning Publications, delves into the complexities of machine learning with a focus on data science. This comprehensive book spans 328 pages and is designed for professional and vocational readers, making it a valuable resource for those looking to deepen their understanding of computing and internet technologies. The book is available in multiple digital formats, including PDF, Kindle, and ePub, catering to various reading preferences. Readers have found this publication informative and beneficial for mastering machine learning concepts, highlighting its clear explanations and practical examples.

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Avantages

  • Comprehensive coverage of machine learning topics
  • Includes practical examples for deeper understanding
  • Available in multiple digital formats
  • Suitable for professional and vocational readers
  • Well-structured for easy navigation

Les inconvénients

  • No collectible edition available

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