Udemy - Learn Streamlit Python

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Learn Streamlit Python [TutsNode.com] -Learn Streamlit Python 08 Project - Building Streamlit Apps - CRUD Apps (Create Read Update Delete)
  • 060 ToDo App in Streamlit - Full Length (CRUD).mp4 (728.9 MB)
  • 060 ToDo App in Streamlit - Full Length (CRUD).en.srt (99.1 KB)
  • 060 todo-app.zip (221.8 KB)
  • 061 ToDo App In Streamlit- Deploying with Streamlit Sharing.en.srt (17.6 KB)
  • 061 ToDo App In Streamlit- Deploying with Streamlit Sharing.mp4 (78.4 MB)
01 Introduction
  • 001 Introduction.en.srt (8.1 KB)
  • 001 Introduction.mp4 (21.3 MB)
  • 002 course-materials-learnstreamlit.zip (105.4 MB)
  • 002 Where to Get Help and Quick Course Guide and Materials.en.srt (8.2 KB)
  • 002 Where to Get Help and Quick Course Guide and Materials.mp4 (29.7 MB)
  • 003 What is Streamlit_.en.srt (3.4 KB)
  • 003 What is Streamlit_.mp4 (9.5 MB)
  • 004 Why Learn Streamlit_.en.srt (2.8 KB)
  • 004 Why Learn Streamlit_.mp4 (6.7 MB)
  • 005 Overview of Streamlit Framework API.en.srt (7.1 KB)
  • 005 Overview of Streamlit Framework API.mp4 (17.5 MB)
  • 006 Setup & Installation In Virutal Environment.en.srt (6.9 KB)
  • 006 Setup & Installation In Virutal Environment.mp4 (26.1 MB)
  • 007 Exploring Streamlit.en.srt (11.4 KB)
  • 007 Exploring Streamlit.mp4 (39.8 MB)
  • 008 Displaying Text In Streamlit.en.srt (12.2 KB)
  • 008 Displaying Text In Streamlit.mp4 (40.9 MB)
  • 009 Behind the Source Code - Inspecting the Text.en.srt (3.6 KB)
  • 009 Behind the Source Code - Inspecting the Text.mp4 (23.5 MB)
  • 010 Working with Colorful Bootstap-Like Text.en.srt (4.7 KB)
  • 010 Working with Colorful Bootstap-Like Text.mp4 (24.9 MB)
  • 011 Displaying Results with St.write() _Superfunction_.en.srt (6.2 KB)
  • 011 Displaying Results with St.write() _Superfunction_.mp4 (26.6 MB)
  • 012 Displaying Pandas DataFrame,Tables and JSON.en.srt (14.1 KB)
  • 012 Displaying Pandas DataFrame,Tables and JSON.mp4 (62.2 MB)
  • 013 Working with Streamlit Widgets - Buttons,Radio Buttons and Checkbox.en.srt (12.2 KB)
  • 013 Working with Streamlit Widgets - Buttons,Radio Buttons and Checkbox.mp4 (53.1 MB)
  • 014 Working with Streamlit Widgets - Select, Multi-select,Sliders and Select Slider.en.srt (10.7 KB)
  • 014 Working with Streamlit Widgets - Select, Multi-select,Sliders and Select Slider.mp4 (51.4 MB)
  • 015 Displaying and Working with Media Files -Images,Audio and Video.en.srt (10.2 KB)
  • 015 Displaying and Working with Media Files -Images,Audio and Video.mp4 (50.3 MB)
  • 016 Working with Text Input - Receiving Input From User.en.srt (13.8 KB)
  • 016 Working with Text Input - Receiving Input From User.mp4 (54.3 MB)
  • 017 How to Configure Streamlit Page.en.srt (17.9 KB)
  • 017 How to Configure Streamlit Page.mp4 (93.4 MB)
  • 018 How to Update Streamlit & How to work with Beta Changes.en.srt (6.1 KB)
  • 018 How to Update Streamlit & How to work with Beta Changes.mp4 (36.6 MB)
  • 019 Plotting In Streamlit _ Using Plotly.en.srt (7.5 KB)
  • 019 Plotting In Streamlit _ Using Plotly.mp4 (38.8 MB)
  • 020 file-upload-app.zip (59.8 KB)
  • 020 Working with File Uploads - Indepth Tutorial.en.srt (61.3 KB)
  • 020 Working with File Uploads - Indepth Tutorial.mp4 (376.7 MB)
  • 021 Structuring Streamlit Apps.en.srt (22.9 KB)
  • 021 Structuring Streamlit Apps.mp4 (88.9 MB)
  • 021 structuring-multiple-streamlit-apps-jcharistech.png (40.3 KB)
02 Module 02 - Data Visualization In Streamlit
  • 022 Plotting In Streamlit-Introduction.en.srt (3.3 KB)
  • 022 Plotting In Streamlit-Introduction.mp4 (7.6 MB)
  • 023 Plotting In Streamlit _ Using St.pyplot For Matplotlib and Others.en.srt (14.1 KB)
  • 023 Plotting In Streamlit _ Using St.pyplot For Matplotlib and Others.mp4 (75.7 MB)
  • 024 Plotting In Streamlit _ Bar Charts, Area Charts and Altair Charts.en.srt (18.7 KB)
  • 024 Plotting In Streamlit _ Bar Charts, Area Charts and Altair Charts.mp4 (100.6 MB)
  • 025 Plotting In Streamlit _ Using Plotly.en.srt (7.5 KB)
  • 025 Plotting In Streamlit _ Using Plotly.mp4 (38.8 MB)
03 Module 02 - Streamlit Components
  • 026 Introduction to Streamlit Components.en.srt (4.5 KB)
  • 026 Introduction to Streamlit Components.mp4 (12.0 MB)
  • 027 Working with Static Streamlit Components - HTML and IFrame.en.srt (24.3 KB)
  • 027 Working with Static Streamlit Components - HTML and IFrame.mp4 (130.0 MB)
04 Module 03 - Project Section - Building Streamlit Apps - NLP Apps
  • 028 Project - NLP & Summarization App.en.srt (8.8 KB)
  • 028 Project - NLP & Summarization App.mp4 (34.0 MB)
  • 029 Project - Summarization App - Structuring the App.en.srt (20.3 KB)
  • 029 Project - Summarization App - Structuring the App.mp4 (96.0 MB)
  • 030 Project - Summarization App -Adding the Summary Process (LexRank and TextRank).en.srt (23.6 KB)
  • 030 Project - Summarization App -Adding the Summary Process (LexRank and TextRank).mp4 (147.2 MB)
  • 031 Project - Summarization App - Evaluating the Extractive Summary with Rouge.en.srt (12.6 KB)
  • 031 Project - Summarization App - Evaluating the Extractive Summary with Rouge.mp4 (88.4 MB)
  • 032 Project - Text Analysis & NLP App.en.srt (7.0 KB)
  • 032 Project - Text Analysis & NLP App.mp4 (36.3 MB)
  • 033 Project -Text Analysis & Spacy App - Structuring the App.en.srt (21.9 KB)
  • 033 Project -Text Analysis & Spacy App - Structuring the App.mp4 (117.4 MB)
  • 034 Project - Text Analysis & Spacy App - Adding the Text Analysis Process.en.srt (24.7 KB)
  • 034 Project - Text Analysis & Spacy App - Adding the Text Analysis Process.mp4 (157.0 MB)
  • 035 Project -Text Analysis & Spacy App - Word Statistics and Sentiment Analysis.en.srt (12.8 KB)
  • 035 Project -Text Analysis & Spacy App - Word Statistics and Sentiment Analysis.mp4 (101.5 MB)
  • 036 Project - Text Analysis & Spacy App - Adding the Plots and Visualizations.en.srt (18.8 KB)
  • 036 Project - Text Analysis & Spacy App - Adding the Plots and Visualizations.mp4 (151.2 MB)
  • 037 Project - Text Analysis & Spacy App - File Download of Results.en.srt (7.1 KB)
  • 037 Project - Text Analysis & Spacy App - File Download of Results.mp4 (54.1 MB)
  • 038 Project - Text Analysis & Spacy App - File Upload (PDF,Txt and Docx).en.srt (20.3 KB)
  • 038 Project - Text Analysis & Spacy App - File Upload (PDF,Txt and Docx).mp4 (168.8 MB)
  • 039 Project - Text Analysis & Spacy

Description


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

Requirements

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

Last Updated 12/2020



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Udemy - Learn Streamlit Python


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Udemy - Learn Streamlit Python


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