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Learn MoreLearn Streamlit Python
Learn Streamlit Python
Create Beautiful Data Apps and Machine Learning Web Apps In Python Faster with Streamlit
Bestseller
Created byJesse E. Agbe
Last updated 10/2025
English
English [Auto],
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
Explore related topics
Course content
14 sections • 125 lectures • 38h 3m total length
- Introduction5:04
learn streamlit to build data apps in hours, covering the data lifecycle, basics of layout and plotting, and deploying models with a client-facing UI.
- Where to Get Help and Quick Course Guide and Materials4:24
Explore where to get help for this course—Google, Discourse, GitHub issues, and Stack Overflow—plus access to course materials in a zip file. Build something with what you learn.
- 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
Explore the streamlit framework api across core, component, experimental, and beta stages, and learn to build interactive dashboards with data frames, markdown, charts, media, and layouts.
- Setup & Installation In Virutal Environment4:21
Set up your workspace for the course by installing Python, choosing a text editor, and creating a virtual environment, then install Streamlit and essential packages via pip.
- 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
Discover how st.write renders text, markdown, and Python code, and learn to feed values and expressions like 1+2 to produce clear outputs.
- Displaying Pandas DataFrame,Tables and JSON8:45
Display pandas data frames, tables, and json in Streamlit by loading pandas data, rendering data frames and static or dynamic tables, and presenting code blocks with syntax highlighting.
- 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
Learn how to plot data in Streamlit using Plotly by creating simple pie charts from a dataset, configuring values and labels, and rendering interactive figures.
- Working with File Uploads - Indepth Tutorial43:05
Learn how to build a Streamlit file upload feature that accepts images, CSV, PDFs, and documents using drag-and-drop, displays file details, and reads content with appropriate libraries.
- Saving Uploaded File into A Directory In Streamlit14:28
Learn how to upload files in a Streamlit app and save them to a directory, using a simple function to write the uploaded file to disk, including images and datasets.
- Working with Multiple File Uploads16:56
- Structuring Streamlit Apps14:20
- Tracking Visited Sections of Streamlit App Via Logging16:44
track every page visit in a streamlit app by implementing a configurable logger that records sessions and navigation across demo, home, and about sections to a file or console.
- 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
Learn how to profile memory and execution time in Streamlit apps using memory profiler, plot memory usage per code line, and optimize performance with practical steps.
- Streamlit Data Editor (New Feature)15:32
Learn to build a Streamlit data editor app that uploads, edits, saves, and downloads a dataframe with in-app editing features.
- 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
Learn to plot in Streamlit using st.pyplot with matplotlib and seaborn, display pandas dataframes with value counts and pivots, and compare old versus recommended figure embedding methods.
- Plotting In Streamlit : Bar Charts, Area Charts and Altair Charts12:21
Learn to plot with Streamlit using bar charts, area charts, and Altair charts by loading datasets like Iris, creating interactive visualizations, and exploring customization and export options.
- 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
Learn to build and render static Streamlit components using HTML and IFrame, including inline CSS and JavaScript, with options for markdown rendering and sizing controls.
- 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
Learn to build multi-page apps in Streamlit using the navigation feature, page objects, and page links. Create home, about, and help pages, and group them with sessions for navigation.
- Streamlitflow - ReactFlow Components21:32
Build networks in Streamlit using the Streamlit flow component and React Flow. Define nodes and edges, manage state, and visualize or upload flows in CSV or JSON with controls.
- 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
Add the LexRank and TextRank based summarization to the Streamlit app, processing a test input to generate a summary, then evaluate it with precision and recall.
- 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
Explore building a Streamlit Python app for text analysis using spaCy and the speci package, including entity extraction, pivot tables, and visual renderings.
- Project -Text Analysis & Spacy App - Word Statistics and Sentiment Analysis10:48
- Project - Text Analysis & Spacy App - Adding the Plots and Visualizations16:23
Learn to build a Streamlit app for text analysis with spaCy, adding plots and visualizations such as word frequency, part-of-speech plots, and word clouds.
- 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
Learn how to build a streamlit text analysis app that uploads pdf, txt, and docx files, reads content with multiple readers, and analyzes text using spaCy.
- Project - Text Analysis & Spacy App - Refactoring and Modularize The App13:15
- Project - Text Analysis & Spacy App - Fixing Insufficient Data For Plot3:58
- Project 03 - Text Analysis App -Demo1:37
Demo for a text analysis app built with Streamlit Python, showing original text preview, word cloud, word frequency, color-coded parts of speech, and stylometry for auto attribution, side by side.
- Project 03 - Text Analysis App -Building the App (Full Length)53:42
Learn to build a Streamlit text analysis app that visualizes stop word handling, part-of-speech tagging, word frequency plots, word clouds, and stylometry in a multi-column interface.
- 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
Build a Streamlit file upload section with drag-and-drop for images, metadata extraction, and display. Create upload sessions, handle binary files, and present combined details in a dataframe.
- 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
Learn to build a metadata extraction app with Streamlit Python, add a downloadable result feature, and use pandas to combine data for download.
- 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
Create a pdf metadata extraction feature in a streamlit app, using file upload, pdf reading, and side-by-side metadata display with download.
- Project 01 - MetaData Extraction App - Analytics and Monitor Section19:52
Develop a metadata extraction app with streamlit that tracks uploaded images, pdfs, and audio, stores metadata in a database, and provides analytics and monitoring of file types and upload status.
- 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
build a machine learning web app to predict diabetes using a UCI dataset and streamlit, and explore data with descriptive stats, distribution plots, heat maps, and interactive predictions.
- 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
Build a diabetes prediction app with Streamlit, integrating machine learning models and dataset attributes, encoding inputs, and creating a user-friendly prediction interface.
- Project - Diabetes Prediction App - Applying the Models For Prediction14:37
- Building the ML Model For Diabetes Prediction -Full Length1:02:13
Learn to predict early-stage diabetes from signs and symptoms using a 520-sample, 17-attribute dataset. Apply data preprocessing, data analysis, feature engineering, and supervised models like logistic regression and random forest.
- ToDo App in Streamlit - Full Length (CRUD)1:09:07
Learn to build a full CRUD to-do app in Streamlit from scratch, including create, read, update, delete operations with a simple database, status tracking, and basic plotting.
- ToDo App In Streamlit- Deploying with Streamlit Sharing11:39
- Simple CRUD App in Streamlit - Demo4:52
Create, read, update, and delete a simple Streamlit-based to-do app, managing tasks with names, statuses, and due dates, while viewing analytics and database updates.
- 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
Implement update and editing flows in a task list app by wiring front-end controls to database updates, letting users select, edit, and save tasks and tax data.
- TaskList CRUD App - Update the Database7:27
- TaskList CRUD App - Deleting Data14:13
Learn how to implement delete data in a Streamlit task list app, deleting from a table with a delete button and previewing data with pandas before removal.
- 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
Build a Streamlit Bible app demo that lets users specify book, chapter, verse, generate a daily verse, search scriptures across versions, visualize entities, and plot part-of-speech analytics, then deploy.
- StreamBible App - Intro & App Structure,Single Verse Section54:12
- StreamBible App - Multiple Verses & Test Analysis Section54:32
Learn to build a Streamlit Bible analysis tool that retrieves multiple verses from a chosen book and chapter and analyzes text with visualizations and tests.
- Refactoring Streamlit Apps From Monolithic to Modular App (Modulith)22:34
Learn how to refactor a monolithic Streamlit app into modular components, creating standalone modules that communicate via simple interfaces to improve maintainability and scalability.
- 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
Build a SQL playground app with Streamlit using Eskil Playground to run queries on country and country language tables and view results as tables or lists.
- QR-Code Generator and Decoder App with Streamlit26:24
Build a qr-code generator and decoder app with Streamlit in Python; upload images, generate and decode QR codes, and view results in a simple, interactive interface.
- 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