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Key benefits

Learn how to showcase machine learning models in a Streamlit application effectively and efficiently

Become an expert Streamlit creator by getting hands-on with complex application creation

Discover how Streamlit enables you to create and deploy apps effortlessly

Description

Streamlit 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.

Who is this book for?

This 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.

What you will learn

Set up your first development environment and create a basic Streamlit app from scratch

Explore methods for uploading, downloading, and manipulating data in Streamlit apps

Create dynamic visualizations in Streamlit using built-in and imported Python libraries

Discover strategies for creating and deploying machine learning models in Streamlit

Use Streamlit sharing for one-click deployment

Beautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebar

Implement best practices for prototyping your data science work with Streamlit

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

AI Assistant (beta) to help accelerate your learning

Frequently bought together

Interactive Dashboards and Data Apps with Plotly and Dash

£38.99

Getting Started with Streamlit for Data Science

£52.99

£29.99

Total £ 121.97

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Amazon verified reviews

Daniel Cajigas Aug 20, 2021

Great book - easy to read and very helpful

Amazon Verified review

xoxo Aug 21, 2021

Broke things down in a very digestible manner. Would recommend for anyone trying to get started with Streamlit.

Adrien Treuille Sep 01, 2021

(Full disclosure: I'm one of the original creators of Streamlit.) Over the past couple years, we've seen several books about Streamlit. This is the first which really captures the *essence* of the project -- how and why Streamlit fundamentally speeds the process of building data app. This book presents a fun, accessible, and completely original take on learning Streamlit. I recommend it for data scientists, machine learning engineers, and Pythonistas of all skill levels!

OB Aug 24, 2021

This is an incredibly helpful book and explains concepts in ways that are easily digestible. Highly recommend to anyone interested in this topic.

Paul Zuo Aug 20, 2021

I'll be honest; my python was not very strong prior to reading this book and the examples here are so thorough and PRACTICAL. I will be using this in my day to day data science work.

Getting Started with Streamlit for Data Science: Create and deploy Streamlit web applications from scratch in Python

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