Why This Course?

Introduction

Ultimately, the output of machine learning (ML) work is geared toward end users who may not know the intricacies involved in its generation.

Toward that end, an interface is needed to help users interact with the ML product in a way that helps solve their problems.

Traditionally, it would require experience in web development to come up with a site with which users can interact.

In this course, we explore the use of Streamlit to build and serve ML applications.

The intended audience

This course is targeted at beginners who want to learn how to develop web-based applications using Streamlit. It’s also suitable for data scientists who are familiar with Python and are interested in developing web-based applications to deploy their ML models.

Prerequisites

A basic knowledge of Python and its concepts is adequate for you to follow and understand the course material.

Learning outcomes

By the end of this course, we’ll achieve the following:

• We’ll learn about Streamlit and its usefulness.
• We’ll learn about Streamlit’s features that help us build applications.
• We’ll learn to deploy an application using Streamlit cloud.