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Beginner's guide to Streamlit with Pythonbuild web-based data and machine learning applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Beginner's guide to Streamlit with Pythonby Sujay Raghavendra.
Reminder of title:	build web-based data and machine learning applications /
Author:	Raghavendra, Sujay.
Published:	Berkeley, CA :Apress :2023.
Description:	xxi, 203 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Web applications.
Online resource:	https://doi.org/10.1007/978-1-4842-8983-9
ISBN:	9781484289839$q(electronic bk.)

Beginner's guide to Streamlit with Pythonbuild web-based data and machine learning applications /
Raghavendra, Sujay.

Beginner's guide to Streamlit with Pythonbuild web-based data and machine learning applications /[electronic resource] :by Sujay Raghavendra. - Berkeley, CA :Apress :2023. - xxi, 203 p. :ill., digital ;24 cm.

1. Introduction to Streamlit -- 2.Table and Chart Elements -- 3.Charts/Visualization -- 4.Data and Media Elements -- 5. Buttons -- 6. Forms -- 7.Navigaations -- 8.Control Flow and Advanced Features -- 9. NLP Project -- 10. Computer Vision Project.

This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you'll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You'll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you'll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. You will: Start developing web applications using Streamlit Understand Streamlit's components Utilize media elements in Streamlit Visualize data using various interactive and dynamic Python libraries Implement models in Streamlit web applications.

ISBN: 9781484289839$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-8983-9doiSubjects--Topical Terms:

609604
Web applications.

LC Class. No.: TK5105.875.I6 / R34 2023

Dewey Class. No.: 006.78

Beginner's guide to Streamlit with Pythonbuild web-based data and machine learning applications /
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520 $a This book will teach you the basics of Streamlit, a Python-based application framework used to build interactive dashboards and machine learning web apps. Streamlit reduces development time for web-based application prototypes of data and machine learning models. As you'll see, Streamlit helps develop data-enhanced analytics, build dynamic user experiences, and showcases data for data science and machine learning models. Beginner's Guide to Streamlit with Python begins with the basics of Streamlit by demonstrating how to build a basic application and advances to visualization techniques and their features. Next, it covers the various aspects of a typical Streamlit web application, and explains how to manage flow control and status elements. You'll also explore performance optimization techniques necessary for data modules in a Streamlit application. Following this, you'll see how to deploy Streamlit applications on various platforms. The book concludes with a few prototype natural language processing apps with computer vision implemented using Streamlit. After reading this book, you will understand the concepts, functionalities, and performance of Streamlit, and be able to develop dynamic Streamlit web-based data and machine learning applications of your own. You will: Start developing web applications using Streamlit Understand Streamlit's components Utilize media elements in Streamlit Visualize data using various interactive and dynamic Python libraries Implement models in Streamlit web applications.
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