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Building Machine Learning Powered Applic

Building Machine Learning Powered Applic

Building Machine Learning Powered Applic
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Building Machine Learning Powered Applic Paperback - 2020

by Emmanuel Ameisen

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Used - Very Good. WOW! Looks Never read, Excellent condition. Soft Cov
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Details

  • Title Building Machine Learning Powered Applic
  • Author Emmanuel Ameisen
  • Binding Paperback
  • Condition Used - Very good
  • Pages 257
  • Volumes 1
  • Language ENG
  • Publisher O'Reilly Media
  • Publication date 2020-02-25
  • Features Index
  • Bookseller's Inventory # 25-03-15-1215
  • ISBN 9781492045113 / 149204511X
  • Weight 0.92 lbs (0.42 kg)
  • Dimensions 9.19 x 7 x 0.55 in (23.34 x 17.78 x 1.40 cm)
  • Category Computers - General Information
  • Library of Congress subjects Application software - Development, Machine learning
  • Dewey Decimal Code 006.31
  • Quantity available 1
  • Bookseller catalogues Self Fulfilled

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Reader reviews for Building Machine Learning Powered Applic

From the publisher

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you€(TM)ll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers€"including experienced practitioners and novices alike€"will learn the tools, best practices, and challenges involved in building a real-world ML application step by step.

Author Emmanuel Ameisen, an experienced data scientist who led an AI education program, demonstrates practical ML concepts using code snippets, illustrations, screenshots, and interviews with industry leaders. Part I teaches you how to plan an ML application and measure success. Part II explains how to build a working ML model. Part III demonstrates ways to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.

This book will help you:

  • Define your product goal and set up a machine learning problem
  • Build your first end-to-end pipeline quickly and acquire an initial dataset
  • Train and evaluate your ML models and address performance bottlenecks
  • Deploy and monitor your models in a production environment

About the author

Emmanuel Ameisen has worked for years as a Data Scientist. He implemented and deployed predictive analytics and machine learning solutions for Local Motion and Zipcar. Recently, Emmanuel has led Insight Data Science's AI program where he oversaw more than a hundred machine learning projects. Emmanuel holds graduate degrees in artificial intelligence, computer engineering, and management from three of France's top schools.

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