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Learn Streamlit Python

Name: Learn Streamlit Python
Rating: 4.5 (719 reviews)

Create Beautiful Data Apps and Machine Learning Web Apps In Python Faster with Streamlit

Bestseller

Created byJesse E. Agbe

Last updated 10/2025

English

What you'll learn

Learn the basics of Streamlit Framework
Use Streamlit to create Machine Learning Web Apps and Data Apps
Deploying Streamlit Python Web Applications

Course content

14 sections • 125 lectures • 38h 3m total length

Introduction5:04
Where to Get Help and Quick Course Guide and Materials4:24
What is Streamlit?2:04
Explore Streamlit, a powerful open source app framework for machine learning and data science that lets you build Python apps, as a bridge from Python code to a UI.
Why Learn Streamlit?1:41
Overview of Streamlit Framework API4:17
Setup & Installation In Virutal Environment4:21
Exploring Streamlit6:56
Explore how to build and run a basic Streamlit app: write a simple main function, run locally on localhost, view in Chrome, and access docs and deployment options.
Displaying Text In Streamlit8:01
Behind the Source Code - Inspecting the Text2:05
Learn how the text and interface are rendered by inspecting the underlying HTML and code behind a Streamlit app, revealing the behind the scenes process.
Working with Colorful Bootstap-Like Text2:58
Displaying Results with St.write() "Superfunction"3:20
Displaying Pandas DataFrame,Tables and JSON8:45
Working with Streamlit Widgets - Buttons,Radio Buttons and Checkbox7:59
Working with Streamlit Widgets - Select, Multi-select,Sliders and Select Slider7:17
Displaying and Working with Media Files -Images,Audio and Video7:06
Working with Text Input - Receiving Input From User8:34
Learn to receive user input in streamlit with text input, number input, max characters, and height. Use time input, password visibility, and color picker with min and max values.
How to Configure Streamlit Page11:43
How to Update Streamlit & How to work with Beta Changes3:41
Learn how to update Streamlit to the latest version and navigate beta changes, including set page config, to maintain backward compatibility while keeping your app current.
Plotting In Streamlit : Using Plotly5:10
Working with File Uploads - Indepth Tutorial43:05
Saving Uploaded File into A Directory In Streamlit14:28
Working with Multiple File Uploads16:56
Structuring Streamlit Apps14:20
Tracking Visited Sections of Streamlit App Via Logging16:44
How to Add File Downloads to Streamlit Apps19:53
Learn how to implement file downloads in Streamlit apps, using a simple function or class with timestamped file names and encoding to enable easy, reliable downloads.
Working with Streamlit Forms21:39
Streamlit-Forms - How to Reset Forms2:57
Learn how to reset Streamlit forms by configuring the submit action to clear test inputs after submission, using declarative input and the default false to true behavior.
Memory Profiling Streamlit Apps7:12
Streamlit Data Editor (New Feature)15:32
Streamlit Chat Input Widget (New Feature)18:15
Streamlit Crash Course (All New Features)1:02:57
⏲️===TimeStamps===⏲️
0:01 Introduction
01:30 Streamlit CLI
02:30 Text Elements
06:12 st.write, markdown
09:35 Error Elements
11:02 Input Widgets
13:15 Date & Number Input
14:57 Radio & Checkbox, Toggle
16:17 Sliders & Selectors
22:08 Data Elements
27:20 Media Elements(Img,Audio,Video)
29:35 Camera Input
32:49 File Upload & Download
35:20 Status Elements (spinner,progress)
37:40 St.toast
38:15 Chat Elements for LLM
42:20 Streaming Text- Typewriter Effect
46:27 Layout
47:04 st.tabs
48:37 st.columns
51:30 Containers in Streamlit
53:20 Expander to hide or show
53:50 Popover & Dialog
55:10 Plotting in Streamlit
58:10 Utils
59:10 St Forms
1:00:20 Streamlit Components
1:01:00 Link Button
1:02:01 Streamlit Session State
1:02:40 Streamlit cloud

Plotting In Streamlit-Introduction2:12
Learn to plot in streamlit with matplotlib, including a line chart, an asteroid map wrapper around jet, and network graphs.
Plotting In Streamlit : Using St.pyplot For Matplotlib and Others9:28
Plotting In Streamlit : Bar Charts, Area Charts and Altair Charts12:21
Plotting In Streamlit : Using Plotly5:10
Watch how to plot in Streamlit using Plotly by rendering simple pie charts from a dataset, selecting language columns as labels and values, and displaying attractive visuals.

Introduction to Streamlit Components2:52
Working with Static Streamlit Components - HTML and IFrame16:04
Streamlit Themes - How to Customize Streamlit with New Themes11:01
Note: Streamlit Themes are available from version 0.79 and upwards so you will have to upgrade or update to get this feature
Streamlit Multi-Pages (Native)20:33
Streamlit Navigation Pages12:54
Streamlitflow - ReactFlow Components21:32
Streamlit Quick Cheatsheets2:02

Project - NLP & Summarization App5:02
Project - Summarization App - Structuring the App14:13
Create a simple streamlit app for text summarization, set up a workspace, add two summarization packages, and build a sidebar menu to trigger summarization and visualize results.
Project - Summarization App -Adding the Summary Process (LexRank and TextRank)14:51
Project - Summarization App - Evaluating the Extractive Summary with Rouge8:51
Project - Text Analysis & NLP App4:18
Build a Streamlit NLP app that analyzes text with named entity recognition, word frequency, parts of speech, sentiment, and word clouds, with drag-and-drop uploads and exportable results.
Project -Text Analysis & Spacy App - Structuring the App17:08
Project - Text Analysis & Spacy App - Adding the Text Analysis Process19:39
Project -Text Analysis & Spacy App - Word Statistics and Sentiment Analysis10:48
Project - Text Analysis & Spacy App - Adding the Plots and Visualizations16:23
Project - Text Analysis & Spacy App - File Download of Results5:22
Learn how to add a downloadable results feature to a Streamlit text analysis app using spaCy, including a function that exports a data frame with a timestamped file name.
Project - Text Analysis & Spacy App - File Upload (PDF,Txt and Docx)16:55
Project - Text Analysis & Spacy App - Refactoring and Modularize The App13:15
Project - Text Analysis & Spacy App - Fixing Insufficient Data For Plot3:58

Project 01 - MetaData Extracton App - Demo6:15
Project 01 - MetaData Extraction App - Setting Up and Structuring the App16:30
Set up and structure a streamlit app that uses drag-and-drop uploads to extract image, audio, and PDF metadata, with clear package usage and a simple home layout.
Project 01 - MetaData Extraction App -Home Section12:01
Project 01 - Building the File Upload Section23:06
Project 01 - MetaData Extraction App - Extraction Process23:45
Extract image metadata via drag-and-drop using Pelo and Pilou, and compile format, description, size, GPS coordinates, and timestamps into a pandas data frame.
Project 01 - MetaData Extraction App - Adding Result Download15:22
Project 01 - MetaData Extracton App - Extracting MetaData From Audio files19:23
Develop a streamlit python project to extract metadata from audio files using mutagen, handling mp3 uploads, and presenting results in a two-column interface for easy inspection.
Project 01 - MetaData Extraction App - Extracting MetaData From PDF Section13:50
Project 01 - MetaData Extraction App - Analytics and Monitor Section19:52
Static Code Analysis & Refactoring Streamlit App17:35
Learn to refactor a streamlit app using static analysis to improve maintainability and complexity, then modularize into utils and db management, and format with black.

Project - Machine Learning Web App - Diabetes Prediction App -Demo5:56
Project - Diabetes Prediction App - Structuring the App14:20
Project -Diabetes Prediction App - Exploratory Data Analysis Section17:09
Project - Diabetes Prediction App - Plotting and Data Visualization27:04
Learn to build a diabetes prediction app user interface in Streamlit by adding and customizing plots, using seaborn for distribution and pie charts, arranging columns, and rendering interactive visualizations.
Project - Diabetes Prediction App - Machine Learning Section13:58
Project - Diabetes Prediction App - Applying the Models For Prediction14:37
Building the ML Model For Diabetes Prediction -Full Length1:02:13

Simple CRUD App in Streamlit - Demo4:52
TaskList CRUD App - Structuring the App15:12
TaskList CRUD App - Create (Adding Data To Database)13:47
Create and connect to a database within the Streamlit app, define a tax table with fields and data types, and implement insert, commit, and select to view stored data.
TaskList CRUD App - Update(Editing From the Front End)17:54
TaskList CRUD App - Update the Database7:27
TaskList CRUD App - Deleting Data14:13
TaskList CRUD App - Reading Data18:02
TaskList CRUD App - Analytics & Plots9:56
Explore the analytics and plotting workflow for a task list app by building value-count plots and a pie chart with Matplotlib, pivoting data, and generating simple visuals.

StreamBible App - Demo3:06
StreamBible App - Intro & App Structure,Single Verse Section54:12
StreamBible App - Multiple Verses & Test Analysis Section54:32
Refactoring Streamlit Apps From Monolithic to Modular App (Modulith)22:34
Email Extractor App - Demo5:25
Email Extractor App - Building the App - Full Length50:46
This is a full length video on building a streamlit app for Extracting Emails,URLs and Phonenumbers
Email Extractor App - Adding Emails to Database (Sqlite3)14:55
Fake Data Generator App39:15
Build a Streamlit Python app to generate fake data across locales for api testing. Customize fields such as username, email, and address, adjust data points, and download the generated datasets.
Course Recommender App46:49
Build a simple course recommendation system app using Udemy Dataset & Streamlit
Text Cleaning App with Streamlit38:22
SQL Playground App with Streamlit24:01
QR-Code Generator and Decoder App with Streamlit26:24
Static Code Analysis App1:11:31

Requirements

Basic understanding of Python programming language
Understand Machine Learning Concepts in Python
Determination and Desire to Learn New Things

Description

Are you having difficulties trying to build web applications for your data science projects? Do you spend more time trying to create a simple MVP app with your data to show your clients and others? Then let me introduce you to Streamlit - a python framework for building web apps.

Welcome to the coolest online resource for learning how to create Data Science Apps and Machine Learning Web Apps using the

awesome Streamlit Framework and Python.

This course will teach you Streamlit - the python framework that saves you from spending days and weeks in creating

data science and machine learning web applications.

In this course we will cover everything you need to know concerning streamlit such as

Fundamentals and the Basics of Streamlit ;

- Working with Text

- Working with Widgets (Buttons,Sliders,

- Displaying Data

- Displaying Charts and Plots

- Working with Media Files (Audio,Images,Video)

- Streamlit Layouts

- File Uploads

- Streamlit Static Components

Creating cool data visualization apps
How to Build A Full Web Application with Streamlit

By the end of this exciting course you will be able to

Build data science apps in hours not days
Productionized your machine learning models into web apps using streamlit
Build some cools and fun data apps
Deploy your streamlit apps using Docker,Heroku,Streamlit Share and more

Join us as we explore the world of building Data and ML Apps.

See you in the Course,Stay blessed.

Tips for getting through the course

Please write or code along with us do not just watch,this will enhance your understanding.
You can regulate the speed and audio of the video as you wish,preferably at -0.75x if the speed is too fast for you.
Suggested Prerequisites is understanding of Python
This course is about Streamlit an ML Framework to create data apps in hours not weeks. We will try our best to cover some concepts for the beginner and the pro .

Who this course is for:

Beginner Python Developers curious about Streamlit
Data Scientist and ML Engineers who want to productionized their Models faster

Learn Streamlit Python

What you'll learn

Explore related topics

Course content

Introduction31 lectures • 5hr 59min

Module 02 - Data Visualization In Streamlit4 lectures • 29min

Module 02 - Streamlit Components & Themes7 lectures • 1hr 27min

Module 03 - Project Section - Building Streamlit Apps - NLP Apps13 lectures • 2hr 31min

Module 03 - Project Section - Building Streamlit Apps - Text Analysis Apps2 lectures • 55min

Project - Building Streamlit Apps -Data Apps10 lectures • 2hr 48min

Module 03 - Project Section - Building Streamlit Apps - Machine Learning Apps7 lectures • 2hr 35min

Project - Building Streamlit Apps - CRUD Apps (Create Read Update Delete)2 lectures • 1hr 21min

Project - Building a CRUD(Create Read Update Delete) App In Streamlit8 lectures • 1hr 41min

Project - Build Streamlit Apps - End to End (StreamBible)13 lectures • 7hr 32min

Requirements

Description

Who this course is for: