This course offers building real-time streamlit applications from scratch in Python.

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Python is the premier programming language for data science and machine learning, but its native strengths for back-end development can leave developers scrambling for a front-end solution. Enter Streamlit, a robust open-source library for developing front-end applications with Python.

This course is a comprehensive introduction to building Streamlit applications integrated with different Python libraries. You’ll be walked through successive projects to create visualizations, display interactive widgets, and customize layouts. You won’t just display data but work on data transformations using Streamlit interfaces and become familiar with basic functionality for native Python applications like caching and placeholders. You’ll wrap up with deploying your application using the Streamlit cloud.

By the end of this course, you’ll be ready to give your Python-based data science and machine learning applications robust user interfaces using Streamlit.

Building the Frontend of Python Web Applications with Streamlit

## Import the libraries
Import the installed libraries and define aliases where needed:

```Python
import streamlit as st
from pytube import YouTube
```
## Header and URL input
We’ll add the header and URL input on the Streamlit web interface.


# Import the libraries
Import the installed libraries and define aliases where needed:

```Python
import streamlit as st
from pytube import YouTube
```
# Header and URL input
We’ll add the header and URL input on the Streamlit web interface.


Learn how to collect the data from YouTube.

Data Collection and Understanding the Stream Object

Introduction

Build a UI for a Model Using Streamlit

Build a Classification Web App using Streamlit and Sklearn

Build a Streamlit App to Scrape Github Profiles

Build a Web App to Transcribe Audio using Python and AssemblyAI

Build a Web App to Generate and Display a Word Cloud of Trending

Build a Web App to Download YouTube Videos in 30 Lines of Code

Deploy Streamlit Apps

Wrapping Up

Data Collection and Understanding the Stream Object

Import the libraries

Header and URL input