Streamlit Deploy Your Data & Ml App On The Web With Python
Published 1/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.96 GB | Duration: 4h 34m
Create in a few hours a great interactive web application and deploy your data or AI model worldwide with Python!
What you'll learn
How to use Streamlit
Develop and deploy a Data application to share a Machine Learning models on the web
Scrape data in real time with an API (Yahoo Finance)
Using the Cloud with Streamlit Cloud
Create an attractive user interface (UI / UX)
Structure your Python program for web development
Know how to optimize a Streamlit application (Cache / Session / Form...)
Using Git and Github to version your code
Overcome the Jupyter Notebook and bring your Data project to life
Requirements
A basic knowledge of the Python programming language is required to better understand the concepts covered in this training. Simple knowledge is sufficient.
No web development and/or data engineering skills are required. All concepts are covered from the beginning.
No experience in the cloud is required. You will learn everything you need to know for the deployment/production part.
Description
Have you ever felt the frustration of having developed a great Machine Learning model on your Jupyter Notebook and never being able to test it against real-world use? That's the core value proposition of Streamlit: To be able to deploy your Data project on the web so that the whole world can use it through your own web application!Thus, all your Data projects will come to life! You will be able to : Share your beautiful image classifier so that other people can use your model by uploading their own images.Deploy the sentiment score of Elon Musk's latest tweets in real time with NLP.Or make interactive dashboards for your corporate teams with an authentication system to restrict access to only a few people.I developed this course after dozens of people contacted me to know how I developed a real-time train reservation web application used by more than 10 000 people. Because yes, you can use streamlit for any kind of application and not only for data / AI applications!In short, hundreds of use cases are possible with streamlit!The great thing about it is that all you need is some knowledge of Python.And that no skills in web development, data engineering or even cloud are necessary.This course is divided into 2 parts: An exercise part where we will see all the fundamentals of Streamlit, from connecting to a database system, through the creation of the interface and finally the part on deployment in the cloud!A second part dedicated to the training project: Development and production of a tracking and analysis application for S&P5O0 stocks, including the visualization of stock price evolution and the calculation of performance indicators. The data will be requested via an API.Take your data projects to the next level with Streamlit!Enjoy the training :) PS : This course is the english version of another french course on streamlit that I put on udemy.
Overview
Section 1: Introduction
Lecture 1 Welcome message!
Lecture 2 Presentation of the training
Lecture 3 What is Streamlit ?
Lecture 4 What you will learn in this course ?
Section 2: Preparing your work environment
Lecture 5 Installation + Github directory download
Lecture 6 Code presentation
Lecture 7 Installation of the virtual environment
Section 3: The foundations of Streamlit
Lecture 8 Presentation
Lecture 9 Exercise part 1 - Streamlit fundamentals
Lecture 10 Exercise part 2 - Streamlit fundamentals
Lecture 11 Final project explanations
Lecture 12 Final Project part 1 - the fundamentalss
Section 4: Interaction with the user (UI / UX)
Lecture 13 Presentation
Lecture 14 Exercise Part 1 - Interaction
Lecture 15 Exercise Part 2 - Interaction
Lecture 16 Project Part 1 - Interaction
Lecture 17 Project Part 2 - Interaction
Section 5: Visualization with Streamlit
Lecture 18 Presentation
Lecture 19 Exercises - visualization
Lecture 20 Project - visualization
Section 6: Advanced features
Lecture 21 Presentation
Lecture 22 Form
Lecture 23 Session
Lecture 24 Cache
Section 7: Application deployment on the web with Streamlit Cloud
Lecture 25 Streamlit Cloud
Section 8: Conclusion
Lecture 26 Conclusion
People who are interested in Data and Python but are frustrated that they can never share their Machine Learning models around them!,Data Scientists in companies who want to share their Machine Learning work or dashboards internally for their collaborators.,Someone who has an idea for a web application project and wants to develop an MVP in a few hours!,All data scientists starting with the production of data applications
Homepage
https://www.udemy.com/course/streamlit-deploy-your-data-ml-app-on-the-web-with-python/Fikper
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