Compartir
Practitioner's Guide to Data Science: Streamlining Data Science Solutions using Python, Scikit-Learn, and Azure ML Service Platform (en Inglés)
Nasir Ali Mirza
(Autor)
·
Bpb Publications
· Tapa Blanda
Practitioner's Guide to Data Science: Streamlining Data Science Solutions using Python, Scikit-Learn, and Azure ML Service Platform (en Inglés) - Mirza, Nasir Ali
$ 1,123.33
$ 1,872.21
Ahorras: $ 748.89
Elige la lista en la que quieres agregar tu producto o crea una nueva lista
✓ Producto agregado correctamente a la lista de deseos.
Ir a Mis Listas
Origen: Estados Unidos
(Costos de importación incluídos en el precio)
Se enviará desde nuestra bodega entre el
Lunes 29 de Julio y el
Viernes 09 de Agosto.
Lo recibirás en cualquier lugar de México entre 1 y 3 días hábiles luego del envío.
Reseña del libro "Practitioner's Guide to Data Science: Streamlining Data Science Solutions using Python, Scikit-Learn, and Azure ML Service Platform (en Inglés)"
"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
