100% Discount || Developing and Deploying Applications with Streamlit

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