Learn Streamlit Python

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Where to Get Help and Quick Course Guide and Materials

Lecture 3: What is Streamlit?

Lecture 4: Why Learn Streamlit?

Lecture 5: Overview of Streamlit Framework API

Lecture 6: Setup & Installation In Virutal Environment

Lecture 7: Exploring Streamlit

Lecture 8: Displaying Text In Streamlit

Lecture 9: Behind the Source Code – Inspecting the Text

Lecture 10: Working with Colorful Bootstap-Like Text

Lecture 11: Displaying Results with St.write() "Superfunction"

Lecture 12: Displaying Pandas DataFrame,Tables and JSON

Lecture 13: Working with Streamlit Widgets – Buttons,Radio Buttons and Checkbox

Lecture 14: Working with Streamlit Widgets – Select, Multi-select,Sliders and Select Slider

Lecture 15: Displaying and Working with Media Files -Images,Audio and Video

Lecture 16: Working with Text Input – Receiving Input From User

Lecture 17: How to Configure Streamlit Page

Lecture 18: How to Update Streamlit & How to work with Beta Changes

Lecture 19: Plotting In Streamlit : Using Plotly

Lecture 20: Working with File Uploads – Indepth Tutorial

Lecture 21: Saving Uploaded File into A Directory In Streamlit

Lecture 22: Working with Multiple File Uploads

Lecture 23: Structuring Streamlit Apps

Lecture 24: Tracking Visited Sections of Streamlit App Via Logging

Lecture 25: How to Add File Downloads to Streamlit Apps

Lecture 26: Working with Streamlit Forms

Lecture 27: Streamlit-Forms – How to Reset Forms

Lecture 28: Memory Profiling Streamlit Apps

Lecture 29: Streamlit Data Editor (New Feature)

Lecture 30: Streamlit Chat Input Widget (New Feature)

Lecture 31: Streamlit Crash Course (All New Features)

Chapter 2: Module 02 – Data Visualization In Streamlit

Lecture 1: Plotting In Streamlit-Introduction

Lecture 2: Plotting In Streamlit : Using St.pyplot For Matplotlib and Others

Lecture 3: Plotting In Streamlit : Bar Charts, Area Charts and Altair Charts

Lecture 4: Plotting In Streamlit : Using Plotly

Chapter 3: Module 02 – Streamlit Components & Themes

Lecture 1: Introduction to Streamlit Components

Lecture 2: Working with Static Streamlit Components – HTML and IFrame

Lecture 3: Streamlit Themes – How to Customize Streamlit with New Themes

Lecture 4: Streamlit Multi-Pages (Native)

Lecture 5: Streamlit Navigation Pages

Chapter 4: Module 03 – Project Section – Building Streamlit Apps – NLP Apps

Lecture 1: Project – NLP & Summarization App

Lecture 2: Project – Summarization App – Structuring the App

Lecture 3: Project – Summarization App -Adding the Summary Process (LexRank and TextRank)

Lecture 4: Project – Summarization App – Evaluating the Extractive Summary with Rouge

Lecture 5: Project – Text Analysis & NLP App

Lecture 6: Project -Text Analysis & Spacy App – Structuring the App

Lecture 7: Project – Text Analysis & Spacy App – Adding the Text Analysis Process

Lecture 8: Project -Text Analysis & Spacy App – Word Statistics and Sentiment Analysis

Lecture 9: Project – Text Analysis & Spacy App – Adding the Plots and Visualizations

Lecture 10: Project – Text Analysis & Spacy App – File Download of Results

Lecture 11: Project – Text Analysis & Spacy App – File Upload (PDF,Txt and Docx)

Lecture 12: Project – Text Analysis & Spacy App – Refactoring and Modularize The App

Lecture 13: Project – Text Analysis & Spacy App – Fixing Insufficient Data For Plot

Chapter 5: Module 03 – Project Section – Building Streamlit Apps – Text Analysis Apps

Lecture 1: Project 03 – Text Analysis App -Demo

Lecture 2: Project 03 – Text Analysis App -Building the App (Full Length)

Chapter 6: Project – Building Streamlit Apps -Data Apps

Lecture 1: Project 01 – MetaData Extracton App – Demo

Lecture 2: Project 01 – MetaData Extraction App – Setting Up and Structuring the App

Lecture 3: Project 01 – MetaData Extraction App -Home Section

Lecture 4: Project 01 – Building the File Upload Section

Lecture 5: Project 01 – MetaData Extraction App – Extraction Process

Lecture 6: Project 01 – MetaData Extraction App – Adding Result Download

Lecture 7: Project 01 – MetaData Extracton App – Extracting MetaData From Audio files

Lecture 8: Project 01 – MetaData Extraction App – Extracting MetaData From PDF Section

Lecture 9: Project 01 – MetaData Extraction App – Analytics and Monitor Section

Lecture 10: Static Code Analysis & Refactoring Streamlit App

Chapter 7: Module 03 – Project Section – Building Streamlit Apps – Machine Learning Apps

Lecture 1: Project – Machine Learning Web App – Diabetes Prediction App -Demo

Lecture 2: Project – Diabetes Prediction App – Structuring the App

Lecture 3: Project -Diabetes Prediction App – Exploratory Data Analysis Section

Lecture 4: Project – Diabetes Prediction App – Plotting and Data Visualization

Lecture 5: Project – Diabetes Prediction App – Machine Learning Section

Lecture 6: Project – Diabetes Prediction App – Applying the Models For Prediction

Lecture 7: Building the ML Model For Diabetes Prediction -Full Length

Chapter 8: Project – Building Streamlit Apps – CRUD Apps (Create Read Update Delete)

Lecture 1: ToDo App in Streamlit – Full Length (CRUD)

Lecture 2: ToDo App In Streamlit- Deploying with Streamlit Sharing

Chapter 9: Project – Building a CRUD(Create Read Update Delete) App In Streamlit

Lecture 1: Simple CRUD App in Streamlit – Demo

Lecture 2: TaskList CRUD App – Structuring the App

Lecture 3: TaskList CRUD App – Create (Adding Data To Database)

Lecture 4: TaskList CRUD App – Update(Editing From the Front End)

Lecture 5: TaskList CRUD App – Update the Database

Lecture 6: TaskList CRUD App – Deleting Data

Lecture 7: TaskList CRUD App – Reading Data

Lecture 8: TaskList CRUD App – Analytics & Plots

Chapter 10: Project – Build Streamlit Apps – End to End (StreamBible)

Lecture 1: StreamBible App – Demo

Lecture 2: StreamBible App – Intro & App Structure,Single Verse Section

Lecture 3: StreamBible App – Multiple Verses & Test Analysis Section

Lecture 4: Refactoring Streamlit Apps From Monolithic to Modular App (Modulith)

Lecture 5: Email Extractor App – Demo

Lecture 6: Email Extractor App – Building the App – Full Length

Lecture 7: Email Extractor App – Adding Emails to Database (Sqlite3)

Lecture 8: Fake Data Generator App