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Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions

MRP: Rs.700.00
Price in points: 700 points
9789352139088
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Minimum quantity for "Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions" is 1.

All Indian Reprints of O'Reilly are printed in Grayscale.

Develop smart applications without spending days and weeks building machine-learning models. With this practical book, you’ll learn how to apply Automated Machine Learning, a process that uses machine learning to help people build machine learning models. Deepak Mukunthu, Parashar Shah, and Wee Hyong Tok provide a mix of technical depth, hands-on examples, and case studies that show how customers are solving real-world problems with this technology.Building machine learning models is an iterative and time-consuming process. Even those who know how to create these models may be limited in how much they can explore. Once you complete this book, you’ll understand how to apply Automated Machine Learning to your data right away.

  • • Learn how companies in different industries are benefiting from Automated Machine Learning
  • • Get started with Automated Machine Learning using Azure
  • • Explore aspects such as algorithm selection, auto featurization, and hyper-parameter tuning
  • • Understand how data analysts, BI professionals, and developers can use Automated Machine Learning in their familiar tools and experiences
  • • Learn how to get started using Automated Machine Learning for use cases including classification and regression.

Books

Author:
Wee Hyong Tok
Author 2:
Deepak Mukunthu
Author 3:
Parashar Shah
Binding:
Paperback
Condition Type:
New
Country Origin:
India
Edition:
First
Gift Wrap:
N
Leadtime to ship in days (default):
Usually ships in 1-2 days
Leadtime to ship in days(if not in stock):
Usually ships in 12-15 days
Pages:
196
Publisher:
Shroff/O'Reilly
Year:
2019

Dimensions

Dimensions (W x H x D):
7 x 9 x .4 inch

Table of Contents (9789352139088_toc.pdf, 173 Kb) [Download]