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- Author: Marco Scutari
- Publisher: CRC Press
- ISBN-10: 0367263505
- ISBN-13: 9780367263508
- Format: 15.6 x 23.4 x 2.1 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
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Description
Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life, yet software engineering has played a remarkably small role compared to other disciplines. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions addresses such a disparity.
Comprising a complete overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software learning practices can be integrated with the workflows of domain experts.
From choosing the right hardware to analysing algorithms and designing scalable architectures, this guide to software engineering will appeal to machine learning and data science specialists, while also utilising natural language and clear case studies to be accessible for students of computer science and aspiring programmers.
EXTRA 10 % discount with code: EXTRA
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The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Marco Scutari
- Publisher: CRC Press
- ISBN-10: 0367263505
- ISBN-13: 9780367263508
- Format: 15.6 x 23.4 x 2.1 cm, hardcover
- Language: English English
Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life, yet software engineering has played a remarkably small role compared to other disciplines. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions addresses such a disparity.
Comprising a complete overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software learning practices can be integrated with the workflows of domain experts.
From choosing the right hardware to analysing algorithms and designing scalable architectures, this guide to software engineering will appeal to machine learning and data science specialists, while also utilising natural language and clear case studies to be accessible for students of computer science and aspiring programmers.
Reviews