Introduction to Streamlit

Learn, Develop and Deploy Streamlit web app for Data Science application using just Python
What you'll learn
Create powerful streamlit apps
Create beautiful web app in minutes
Build Web App without knowing anything on HTML, CSS, Javascrip
Develop Web Apps in Python
Develop data science web app
Requirements
Beginner to Python
Must know Pandas for Data Analysis
Description
Welcome to the course Introduction to
Streamlit is an open-source Python library that makes it easy to create and share beautiful, custom web apps for machine learning and data science that can be used to share analytics results, build complex interactive experiences, and illustrate new machine learning models. In just a few minutes you can build and deploy powerful data apps.
On top of that, developing and deploying Streamlit apps is incredibly fast and flexible, often turning application development time from days into hours.
In this course, we start out with the Streamlit basics. We will learn how to download and run demo Streamlit apps, how to edit demo apps using our own text editor, how to organize our Streamlit apps, and finally, how to make our very own. Then, we will explore the basics of data visualization in Streamlit. We will learn how to accept some initial user input, and then add some finishing touches to our own apps with text. At the end of this course, you should be comfortable starting to make your own Streamlit applications.
In particular, we will cover the following topics
Why Streamlit?
Installing Streamlit
Organizing Streamlit apps
Streamlit plotting demo
Making an app from scratch
Who this course is for
Data Scientist who want to present Data Analysis and machine learning models
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