Low-Code AI: A Practical Project-Driven Introduction to Machine Learning (Record no. 98177)

MARC details
000 -LEADER
fixed length control field 01986 a2200181 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 231222b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9789355425560
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.74015
Cutter STR
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Stripling Gwendolyn
245 ## - TITLE STATEMENT
Title Low-Code AI: A Practical Project-Driven Introduction to Machine Learning
250 ## - EDITION STATEMENT
Edition statement 1st
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Shroff/O’Reilly
Date of publication, distribution, etc 2023
Place of publication, distribution, etc India
300 ## - PHYSICAL DESCRIPTION
Extent 328
520 ## - Remark
Summary, etc Take a data-first and use-case–driven approach with Low-Code AI to understand machine learning and deep learning concepts. This hands-on guide presents three problem-focused ways to learn no-code ML using AutoML, low-code using BigQuery ML, and custom code using scikit-learn and Keras. In each case, you'll learn key ML concepts by using real-world datasets with realistic problems.<br/><br/>Business and data analysts get a project-based introduction to ML/AI using a detailed, data-driven approach: loading and analyzing data; feeding data into an ML model; building, training, and testing; and deploying the model into production. Authors Michael Abel and Gwendolyn Stripling show you how to build machine learning models for retail, healthcare, financial services, energy, and telecommunications.<br/><br/>You'll learn how to:<br/><br/>Distinguish between structured and unstructured data and the challenges they present<br/>Visualize and analyze data<br/>Preprocess data for input into a machine learning model<br/>Differentiate between the regression and classification supervised learning models<br/>Compare different ML model types and architectures, from no code to low code to custom training<br/>Design, implement, and tune ML models<br/>Export data to a GitHub repository for data management and governance<br/><br/>Source: https://www.amazon.in/Low-Code-Practical-Project-Driven-Introduction-Grayscale/dp/9355425562/ref=sr_1_1?crid=3AYZ4GS9OSL8W&keywords=9789355425560&qid=1703238978&sprefix=9789355423498%2Caps%2C364&sr=8-1
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Abel Michael
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 Programme Full call number Barcode Date last seen Date last borrowed Cost, replacement price Koha item type
    Dewey Decimal Classification     Reference book Main Library Main Library Analytics 06/12/2023 Shroff Publisher 1840.00 Indian Book   005.74015 STR 119110 27/02/2025 21/02/2025 2300.00 Book

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