Building Machine Learning Powered Applications: Going from Idea to Product (Record no. 98464)

MARC details
000 -LEADER
fixed length control field 02026 a2200181 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 240304b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789352139613
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74015
Cutter AME
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ameisen Emmanuel
245 ## - TITLE STATEMENT
Title Building Machine Learning Powered Applications: Going from Idea to Product
250 ## - EDITION STATEMENT
Edition statement 1st
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Shroff/O'Reilly
Date of publication, distribution, etc 2020
Place of publication, distribution, etc India
300 ## - PHYSICAL DESCRIPTION
Extent 260
520 ## - Remark
Summary, etc Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’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.<br/><br/>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.<br/><br/>This book will help you:<br/><br/>Define your product goal and set up a machine learning problem<br/>Build your first end-to-end pipeline quickly and acquire an initial dataset<br/>Train and evaluate your ML models and address performance bottlenecks<br/>Deploy and monitor your models in a production environment<br/><br/>Source: https://www.amazon.in/Building-Machine-Learning-Powered-Applications/dp/9352139615/ref=sr_1_1?crid=3VMFHYXJC9AU7&dib=eyJ2IjoiMSJ9.2sasGNakDztkFveQVheePw.ONk-MOT59OcjpJYcxMhp35NHwCn9Q1Ex62yaJCuspds&dib_tag=se&keywords=9789352139613&qid=1709564247&sprefix=9789355421982%2Caps%2C257&sr=8-1
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Machine Learning
906 ## - LOCAL DATA ELEMENT F, LDF (RLIN)
a Business Analytics
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Item type Book
Source of classification or shelving scheme Dewey Decimal Classification
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Collection Type Full call number Barcode Date last seen Cost, replacement price Koha item type
    Dewey Decimal Classification     Reference book Main Library Main Library Analytics 29/01/2024 Amazon 1000.00 Foreign Book 005.74015 AME 119208 29/01/2024 1000.00 Book

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