Practitioner's Guide to Data Science | Streamlining Data Science Solutions using Python, Scikit-Learn, and Azure ML Service Platform
Language: English
Published by BPB Publications, 2022
- Softcover
- New
Seller: preigu, Osnabrück, Germanypreigu
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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.
Seller Inventory # 121156092
- Title
- Practitioner's Guide to Data Science | Streamlining Data Science Solutions using Python, Scikit-Learn, and Azure ML Service Platform
- Author
- Nasir Ali Mirza
- Publisher
- BPB Publications
- Publication year
- 2022
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9391392873
- ISBN 13
- 9789391392871
- Item weight
- 464 grams
- Dimensions
- 235 x 191 x 14 mm
- Seller catalogs
- Bücher
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.
Description
"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.
Who this book is for
This book is intended for programmers who wish to pursue AI/ML development and build a solid conceptual foundation and familiarity with related processes and frameworks. Additionally, this book is an excellent resource for Software Architects and Managers involved in the design and delivery of Data Science-based solutions.
Table of Contents
1. Data Science for Business
2. Data Science Project Methodologies and Team Processes
3. Business Understanding and Its Data Landscape
4. Acquire, Explore, and Analyze Data
5. Pre-processing and Preparing Data
6. Developing a Machine Learning Model
7. Lap Around Azure ML Service
8. Deploying and Managing Models
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
Shipping rates from Germany to U.S.A.
| Item | 60 to 60 business days | 60 to 60 business days |
|---|---|---|
| First item | £ 60.20 | £ 60.20 |
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