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Thinking Data Science

A Data Science Practitioner’s Guidebook

Produktform: E-Buch Text Elektronisches Buch in proprietärem

The challenge for a data scientist is to extract meaningful information from huge datasets that will help to create better strategies for businesses. Many Machine Learning algorithms and Neural Networks are designed to do analytics on such datasets. For a data scientist, it is a daunting decision as to which algorithm to use for a given dataset. Although there is no single answer to this question, a systematic approach to problem solving is necessary. This book describes the various ML algorithms conceptually and defines/discusses a process in the selection of ML/DL models. The consolidation of available algorithms and techniques for designing efficient ML models is the key aspect of this book.  will help practising data scientists, academicians, researchers, and students who want to build ML models using the appropriate algorithms and architectures, whether the data be small or big. weiterlesen

Dieser Artikel gehört zu den folgenden Serien

Elektronisches Format: PDF

Sprache(n): Englisch

ISBN: 978-3-031-02363-7 / 978-3031023637 / 9783031023637

Verlag: Springer International Publishing

Erscheinungsdatum: 01.03.2023

Seiten: 358

Autor(en): Poornachandra Sarang

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