Complete Guide to Data Science Applications with Streamlit

Description :

Analyzing data and building machine learning models is one thing. Packaging these analyses and models such that they are sharable is a different ball game altogether.

This course aims at teaching you the fastest and easiest way to build and share data applications using Streamlit. You don’t need any experience in building front-end applications for this. Here are some of the things you can expect to cover in this course:

Python Crash Course

NumPy Crash Course

Introduction to Streamlit

Integrating Matplotlit and Seaborn in Streamlit

Using Altair and Vega-Lite in Streamlit

Understand all Streamlit Widgets

Upload and Process Files

Build an Image Processing Application

Develop a Natural Language Processing Application

Integrate Maps with Streamlit

Implement Plotly Graphs

Authenticate Your Applications

Laying Out your Application in Streamlit

Developing with Streamlit Components

Deploying Data Applications