Developing and Deploying Applications with Streamlit

Last updated 12/2022

Duration: 3h 35m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 1.27 GB

Genre: eLearning | Language: English[Auto]

The 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

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

Installing Anaconda and create a virtual env

Installing Streamlit , pytube, firebase

Setting up GitHub account if you already don't have one

Display Information with Streamlit

Widgets with Streamlit

Working with data frames ( Loading , Displaying )

Creating a image filter ( we use popular Instagram filters)

Creating a YouTube video downloader (using pytube api)

pytube is a lightweight, dependency-free Python library which is used for downloading videos from the web

Creating Interactive plots

User selected input value for chart

Animated Plot

Introduction to Multipage Apps

Structuring multipage apps

Run a multipage app

Adding pages

Build a OCR - Image to text conversion with tesseract

Build a World Cloud App

ChatGPT + Streamlit

Build a auto review response generator with chatGPT and Open AI

Build a Leetcode problem solver with chatGPT and Open AI

Creating a personal portfolio page with streamlit

Deploy Application with Streamlit Cloud

Content in progress to be uploaded soon

Concept of Sessions

NTLK with streamlit

Working with SQLite

Connecting to database

Reading data from database

Writing Data into database

Additional Apps

Static Code quality analyzer

No SQL Job Board with Firebase API

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