Developing and Deploying Applications with Streamlit


Developing and Deploying Applications with StreamlitThe fastest way to build and share data apps.

What you’ll learn

  • Streamlit and its usefulness.
  • Streamlit’s features that help up build web , data and machine learning application
  • Deploying streamlit applications on streamlit cloud
  • Personal Portfolio page hosted on streamlit cloud


  • Basic knowledge of Python programing language
  • Willingness to learn or know SciKit Learn
  • Basic knowledge of HTML CSS
  • Willingness to learn or have prior knowledge of GitHub


Streamlit is an open-source app framework for Machine Learning and Data Science teams.

Streamlit lets you turn data scripts into shareable web apps in minutes. It’s all Python, open-source, and free! And once you’ve created an app you can use our cloud platform to deploy, manage, and share your app!

In this course we will cover everything you need to know concerning streamlit such as

  1. Installing Anaconda and create a virtual env
  2. Installing Streamlit , pytube, firebase
  3. Setting up GitHub account if you already don’t have one
  4. Display Information with Streamlit
  5. Widgets with Streamlit
  6. Working with data frames ( Loading , Displaying )
  7. Creating a image filter ( we use popular Instagram filters)
  8. Creating a YouTube video downloader (using pytube api)
    1. pytube is a lightweight, dependency-free Python library which is used for downloading videos from the web
  9. Creating Interactive plots
    1. User selected input value for chart
    2. Animated Plot
  10. Introduction to Multipage Apps
    1. Structuring multipage apps
    2. Run a multipage app
    3. Adding pages
  11. Build a OCR – Image to text conversion with tesseract
  12. Build a World Cloud App
  13. ChatGPT + Streamlit
    1. Build a auto review response generator with chatGPT and Open AI
    2. Build a Leetcode problem solver with chatGPT and Open AI
  14. Creating  a personal portfolio page with streamlit
  15. Deploy Application with Streamlit  Cloud
  16. Content in progress to be uploaded soon
    1. Concept of Sessions
    2. NTLK with streamlit
    3. Working with SQLite
      1. Connecting to database
      2. Reading data from database
      3. Writing Data  into database
    4. Additional Apps
      1. Static Code quality analyzer
      2. No SQL Job Board with Firebase  API
      3. Converting random forest model into streamlit application

Who this course is for:

  • Anyone who is interested Python and Machine Learning
  • If you want to have a free portfolio page

Created by Avinash A
Last updated 2/2023
English [Auto]

Size: 1.27 GB

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