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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 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
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 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
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 244 |
| ISBN-13: | 9789391392871 |
| ISBN-10: | 9391392873 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Mirza, Nasir Ali |
| Hersteller: | BPB Publications |
| Maße: | 235 x 191 x 14 mm |
| Von/Mit: | Nasir Ali Mirza |
| Erscheinungsdatum: | 17.01.2022 |
| Gewicht: | 0,464 kg |
| Erscheinungsjahr: | 2022 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 244 |
| ISBN-13: | 9789391392871 |
| ISBN-10: | 9391392873 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Mirza, Nasir Ali |
| Hersteller: | BPB Publications |
| Maße: | 235 x 191 x 14 mm |
| Von/Mit: | Nasir Ali Mirza |
| Erscheinungsdatum: | 17.01.2022 |
| Gewicht: | 0,464 kg |
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