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Published by BPB Publications 2022-01, 2022
ISBN 10: 9391392873 ISBN 13: 9789391392871
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Published by BPB Publications, IN, 2022
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Language: English
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Taschenbuch. Condition: Neu. Practitioner's Guide to Data Science | Streamlining Data Science Solutions using Python, Scikit-Learn, and Azure ML Service Platform | Nasir Ali Mirza | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | BPB Publications | EAN 9789391392871 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 'How is the Data Science project to be implemented ' has never been more conceptually sounding, thanks to the work presented in this book. This book provides an in-depth look at the current state of the world's data and how Data Science plays a pivotal role in everything we do.This book explains and implements the entire Data Science lifecycle using well-known data science processes like CRISP-DM and Microsoft TDSP. The book explains the significance of these processes in connection with the high failure rate of Data Science projects.The book helps build a solid foundation in Data Science concepts and related frameworks. It teaches how to implement real-world use cases using data from the HMDA dataset. It explains Azure ML Service architecture, its capabilities, and implementation to the DS team, who will then be prepared to implement MLOps. The book also explains how to use Azure DevOps to make the process repeatable while we're at it.By the end of this book, you will learn strong Python coding skills, gain a firm grasp of concepts such as feature engineering, create insightful visualizations and become acquainted with techniques for building machine learning models.TABLE OF CONTENTS1. Data Science for Business2. Data Science Project Methodologies and Team Processes3. Business Understanding and Its Data Landscape4. Acquire, Explore, and Analyze Data5. Pre-processing and Preparing Data6. Developing a Machine Learning Model7. Lap Around Azure ML Service8. Deploying and Managing Models.