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A Guide to Applied Machine Learning for Biologists / / edited by Mohammad "Sufian" Badar



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Titolo: A Guide to Applied Machine Learning for Biologists / / edited by Mohammad "Sufian" Badar Visualizza cluster
Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Edizione: 1st ed. 2023.
Descrizione fisica: 1 online resource (272 pages)
Disciplina: 170
570.285631
Soggetto topico: Bioinformatics
Biology
Biotechnology
Microbiology
Biological Sciences
Persona (resp. second.): BadarMohammad Sufian
Nota di bibliografia: Includes bibliographical references and index.
Nota di contenuto: Basics of Modern Computer Systems (Unix/Linux Centric) -- The Python Programming Language ( A tool to enter the world of Machine Learning) -- Basic Math -- Introduction to the World of Bioinformatics -- Introduction to Artificial Intelligence & ML -- Fundamentals of ML -- Applications in the field of Bioinformatics -- Case Studies and a Minor Project -- Future Prospects -- Further Readings.
Sommario/riassunto: This textbook is an introductory guide to applied machine learning, specifically for biology students. It familiarizes biology students with the basics of modern computer science and mathematics and emphasizes the real-world applications of these subjects. The chapters give an overview of computer systems and programming languages to establish a basic understanding of the important concepts in computer systems. Readers are introduced to machine learning and artificial intelligence in the field of bioinformatics, connecting these applications to systems biology, biological data analysis and predictions, and healthcare diagnosis and treatment. This book offers a necessary foundation for more advanced computer-based technologies used in biology, employing case studies, real-world issues, and various examples to guide the reader from the basic prerequisites to machine learning and its applications.
Titolo autorizzato: Guide to Applied Machine Learning for Biologists  Visualizza cluster
ISBN: 3-031-22206-7
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910734855603321
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