Basics of Streamlit

Name: Basics of Streamlit
Rating: 4.5 (2 reviews)

Create web application for your Data Science / Machine Learning project with pure python!

Created byHarsh Gupta

Last updated 9/2021

English

English [Auto],

What you'll learn

Basics of Streamlit
Utilizing the various components of Streamlit
Creating projects using Streamlit
Deploying Streamlit application

Course content

4 sections • 9 lectures • 1h 51m total length

Introduction to Streamlit5:40
Discover streamlet, a Python framework for building web dashboards, deployable on Heroku, and learn to create a salary dashboard with interactive filters and a to-do task manager.

Displaying text9:35
Explore how to display text in a Streamlet app by adding titles, headers, subheaders, markdown, and preformatted text, along with images and latex.
Displaying data14:03
Demonstrate displaying data in streamlit by using lists, dictionaries, dataframes, and json, and introduce the caching decorator to speed up repeated computations in your app.
Displaying plots14:51
Explore visualizing data in streamlit by plotting line, area, bar, and scatter charts with interactive features, and add maps and media—images, audio, and YouTube videos—using matplotlib and Altair.

Adding widgets12:45
Learn to build an interactive streamlit app with widgets, including buttons, text inputs, date and time inputs, checkboxes, radios, selects, and file uploads, to trigger actions and display results.
Sidebar, Navigation bar and Status14:24
Learn to add widgets to the streamlit sidebar, build a navigation bar for multi-page views, and display status messages, progress bars, and balloon animations to enhance a data app.
Adding layouts8:40
Learn to use the new column layout in Streamlit 0.68.0 to place multiple elements on the same line and build a two-column registration form.
Change colour scheme of the app3:39
Discover how to change color scheme of a streamlet app using the theme option in streamlet 0.7.9+, customize colors, and save the theme in a config file for project defaults.

Requirements

Basic understanding of python programing

Description

Creating a web app and deploying the ML model is the toughest part since most the data science enthusiasts are not familiar with web dev. But Streamlit has made the job extremely easy. Being a Data Science enthusiast, I would like to share and contribute my learnings to the community!

This course introduces you to the basics of Streamlit library. Create professional-looking web applications with python without the knowledge of HTML/CSS.

The course introduces the basic elements of streamlit:

Adding Text and Data to your app
Plotting Graphs
Creating layout
Adding widgets
Changing the theme of your web application
Create a basic web app to understand how to combine streamlit and Machine learning models.

By the end of the course, you will be able to

create your own web app within few hours
create a user interface for your ML models
deploy your web application

Before you begin, I would suggest you to brush up on your python skills.

I would also suggest coding along for better learning and understanding!

This course is for absolute beginners who want to understand how streamlit works.

I have tried to keep things as simple as possible and therefore you might not find me using the best coding practices.

I prefer using VS Code for coding but feel free to use your favourite editor!

Who this course is for:

Beginner python programmer and data science enthusiast

Basics of Streamlit

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 6min

Displaying text, data and plots using Streamlit3 lectures • 38min

Adding widgets, sidebar and layouts4 lectures • 39min

First web app using Streamlit1 lecture • 28min

Requirements

Description

Who this course is for: