Overview
- Written for both aspiring and working data scientists to develop and improve their AI applications
- Teaches how to handle numeric, text and image datasets, GOFAI and ANN/DNN development, and use automated tools
- Includes a large section on clustering algorithms, explaining their applications for various sized datasets
Part of the book series: The Springer Series in Applied Machine Learning (SSAML)
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Table of contents (20 chapters)
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Front Matter
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Part III
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Front Matter
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Keywords
About this book
This definitive guide to Machine Learning projects answers the problems an aspiring or experienced data scientist frequently has: Confused on what technology to use for your ML development? Should I use GOFAI, ANN/DNN or Transfer Learning? Can I rely on AutoML for model development? What if the client provides me Gig and Terabytes of data for developing analytic models? How do I handle high-frequency dynamic datasets? This book provides the practitioner with a consolidation of the entire data science process in a single “Cheat Sheet”.
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. Thinking Data Science 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.
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Thinking Data Science
Book Subtitle: A Data Science Practitioner’s Guide
Authors: Poornachandra Sarang
Series Title: The Springer Series in Applied Machine Learning
DOI: https://doi.org/10.1007/978-3-031-02363-7
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-02362-0Published: 02 March 2023
Softcover ISBN: 978-3-031-02365-1Published: 02 March 2024
eBook ISBN: 978-3-031-02363-7Published: 01 March 2023
Series ISSN: 2520-1298
Series E-ISSN: 2520-1301
Edition Number: 1
Number of Pages: XX, 358
Number of Illustrations: 101 b/w illustrations, 132 illustrations in colour
Topics: Machine Learning, Data Structures and Information Theory, Artificial Intelligence