| Description |
1 online resource |
| Contents |
Table of Contents An Introduction to Streamlit Uploading, Downloading, and Manipulating Data Data Visualization Using Machine Learning with Streamlit Deploying Streamlit with Streamlit Sharing Beautifying Streamlit Apps Exploring Streamlit Components Deploying Streamlit Apps with Heroku and AWS Improving Job Applications With Streamlit The Data Project - Prototyping Projects in Streamlit Using Streamlit for Teams Streamlit Power Users |
| Summary |
Create, deploy, and test your Python applications, analyses, and models with ease using StreamlitKey FeaturesLearn how to showcase machine learning models in a Streamlit application effectively and efficientlyBecome an expert Streamlit creator by getting hands-on with complex application creationDiscover how Streamlit enables you to create and deploy apps effortlesslyBook DescriptionStreamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time. You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you'll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps. By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python. What you will learnSet up your first development environment and create a basic Streamlit app from scratchExplore methods for uploading, downloading, and manipulating data in Streamlit appsCreate dynamic visualizations in Streamlit using built-in and imported Python librariesDiscover strategies for creating and deploying machine learning models in StreamlitUse Streamlit sharing for one-click deploymentBeautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebarImplement best practices for prototyping your data science work with StreamlitWho this book is forThis book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you're a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered |
| Notes |
Online resource; title from digital title page (viewed on October 22, 2021) |
| Subject |
Application software -- Development.
|
|
Python (Computer program language)
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Application software -- Development
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Python (Computer program language)
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| Form |
Electronic book
|
| ISBN |
1800563205 |
|
9781800563209 |
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