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Building the Frontend of Python Web Applications with Streamlit

Build robust front-end Python web applications with Streamlit. Explore integrating Python libraries, creating visualizations, interactive widgets, and deploying on Streamlit cloud.

5.0

31 Lessons

3h 25min

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LEARNING OBJECTIVES

A working knowledge of the training and testing methods for machine learning models in Python
A strong foundation in developing user interfaces (UI) using the Streamlit framework
An understanding of translating various data science libraries, including Scikit-learn, into a front-end UI
A deep familiarity with various web scraping techniques to gather various data and present it in a native web application
Hands-on experience using AssemblyAI, Streamlit, and Heroku to develop, skin, and deploy complete web applications

Learning Roadmap

31 Lessons1 Quiz

Introduction

Get familiar with building user-friendly, responsive web apps using Streamlit for data science.

Streamlit Overview

Build a UI for a Model Using Streamlit

Walk through building an interactive Streamlit UI for predicting outcomes using logistic regression models.

Prerequisites and Required Libraries

Build a Logistic Regression Model

Build the DataFrame and Model Performance UI with Streamlit

Build User Input UI with Streamlit

Exercise 1: Build UI for Candy Bar Prediction

Solution: Build UI for Candy Bar Prediction

Sentiment Analysis Using Multinomial Logistic Regression

Project

Premium

Build a Classification Web App using Streamlit and Sklearn

5 Lessons

Master the steps to build a classification web app with Streamlit and Scikit-learn.

Build a Streamlit App to Scrape Github Profiles

3 Lessons

Grasp the fundamentals of building a Streamlit app to scrape detailed GitHub profiles.

Build a Web App to Transcribe Audio using Python and AssemblyAI

3 Lessons

Map out the steps for creating an audio transcription app with Streamlit and AssemblyAI.

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

6 Lessons

Follow the process of generating dynamic word clouds using Streamlit, Beautiful Soup, and custom user inputs.

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

3 Lessons

Master the steps to build a YouTube downloader app with Streamlit and pytube.

Deploy Streamlit Apps

2 Lessons

Learn how to use Streamlit Sharing and Heroku for deploying Streamlit apps.

Wrapping Up

2 Lessons

Look at concluding your understanding of Streamlit UI components and web app deployment.

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

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.

ABOUT THE AUTHOR

Rahul Banerjee

Energetic and passionate 4th-year Computer Engineering student with experience in working in an Agile environment. Experienced in React, Flask, JavaScript, and SQL.

Learn more about Rahul

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