Practitioner’s Guide to Data Science: Streamlining Data Science Solutions using Python, Scikit-Learn, and Azure ML Service Platform (English Edition)
Author: Nasir Ali Mirza(Author)
Publisher finelybook 出版社: BPB Publications (January 17, 2022)
Language 语言: English
Print Length 页数: 242 pages
ISBN-10: 9391392873
ISBN-13: 9789391392871
Book Description
Covers Data Science concepts, processes, and the real-world hands-on use cases.
Key Features
Covers the journey from a basic programmer to an effective Data Science developer.
Applied use of Data Science native processes like CRISP-DM and Microsoft TDSP.
Implementation of MLOps using Microsoft Azure DevOps.
“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.
What you will learn
Organize Data Science projects using CRISP-DM and Microsoft TDSP.
Learn to acquire and explore data using Python visualizations.
Get well versed with the implementation of data pre-processing and Feature Engineering.
Understand algorithm selection, model development, and model evaluation.
Hands-on with Azure ML Service, its architecture, and capabilities.
Learn to use Azure ML SDK and MLOps for implementing real-world use cases.
