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Developing and Deploying Applications with Streamlit
Rating: 4.1 out of 5(65 ratings)
17,685 students

Developing and Deploying Applications with Streamlit

The fastest way to build and share data apps.
Created byAvinash A
Last updated 2/2023
English
English [Auto],

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

Course content

16 sections • 44 lectures • 3h 48m total length

  • Setting up Anaconda and GitHub2:49
  • Creative a virtual env in anaconda12:30
  • Test Streamlit is working0:51

Requirements

  • 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

Description

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. Adding Authentication to your  Streamlit app using Streamlit-Authenticator

    1. Authentication via Pickle File

    2. Authentication via  Database

  12. Build a Word Cloud App

  13. Build a OCR - Image to text conversion with tesseract

  14. Build a World Cloud App

  15. 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 

  16. Content in progress to be uploaded soon

    1. Creating  a personal portfolio page with streamlit

    2. Deploy Application with Streamlit  Cloud

    3. Concept of Sessions

    4. NTLK with streamlit

    5. Working with SQLite

      1. Connecting to database

      2. Reading data from database

      3. Writing Data  into database

    6. 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

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