Learn Streamlit Python

Learn Streamlit Python, available at $64.99, has an average rating of 4.29, with 120 lectures, based on 570 reviews, and has 4862 subscribers.

You will learn about Learn the basics of Streamlit Framework Use Streamlit to create Machine Learning Web Apps and Data Apps Deploying Streamlit Python Web Applications This course is ideal for individuals who are Beginner Python Developers curious about Streamlit or Data Scientist and ML Engineers who want to productionized their Models faster It is particularly useful for Beginner Python Developers curious about Streamlit or Data Scientist and ML Engineers who want to productionized their Models faster.

Enroll now: Learn Streamlit Python

Summary

Title: Learn Streamlit Python

Price: $64.99

Average Rating: 4.29

Number of Lectures: 120

Number of Published Lectures: 120

Number of Curriculum Items: 120

Number of Published Curriculum Objects: 120

Original Price: $59.99

Quality Status: approved

Status: Live

What You Will Learn

Learn the basics of Streamlit Framework

Use Streamlit to create Machine Learning Web Apps and Data Apps

Deploying Streamlit Python Web Applications

Who Should Attend

Beginner Python Developers curious about Streamlit

Data Scientist and ML Engineers who want to productionized their Models faster

Target Audiences

Beginner Python Developers curious about Streamlit

Data Scientist and ML Engineers who want to productionized their Models faster

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 .

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

Instructors

Jesse E. Agbe
Developer

Rating Distribution

1 stars: 14 votes

2 stars: 8 votes

3 stars: 62 votes

4 stars: 155 votes

5 stars: 331 votes

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