Streamlit : Deploy your Data & ML app on the web with Python

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Pierre-louis Danieau

4:32:34

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1 - Introduction

1 - Initial Quizz.html

1 - Welcome message.html

1 - Windows.pdf

1 - streamlit-presentation.pdf

2 - Presentation of the training.mp4

01:55

3 - What is Streamlit.mp4

08:29

4 - What you will learn in this course.mp4

06:32

2 - Preparing your work environment

5 - Installation Github directory download.mp4

03:23

5 - Windows.pdf

6 - Code presentation.mp4

05:33

7 - Installation of the virtual environment.mp4

06:34

3 - The foundations of Streamlit

8 - Presentation.mp4

04:55

9 - Exercise part 1 Streamlit fundamentals.mp4

23:43

10 - Exercise part 2 Streamlit fundamentals.mp4

22:27

11 - Final project explanations.html

12 - Final Project part 1 the fundamentalss.mp4

16:04

4 - Interaction with the user UI UX

13 - Presentation.mp4

05:45

14 - Exercise Part 1 Interaction.mp4

15:12

15 - Exercise Part 2 Interaction.mp4

11:18

16 - Project Part 1 Interaction.mp4

20:37

17 - Project Part 2 Interaction.mp4

19:51

5 - Visualization with Streamlit

18 - Presentation.mp4

04:27

19 - Exercises visualization.mp4

16:13

20 - Project visualization.mp4

31:35

6 - Advanced features

21 - Presentation.mp4

01:48

22 - Form.mp4

12:27

23 - Session.mp4

09:24

24 - Cache.mp4

11:37

7 - Application deployment on the web with Streamlit Cloud

25 - Streamlit Cloud.mp4

11:05

8 - Conclusion

26 - Conclusion.mp4

01:40

Description

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

Who is this for?

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

More details

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 streamlitÂ that I put on udemy.
Who this course is for:
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

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French guy passionate about data and machine learning, I am currently working as a Data Scientist in a fintech in Paris. I have an engineering degree from ENSTA and I have worked as a Data Scientist in big companies such as Thales and Crédit Agricole or fintech startups like myfo and PayLead.I am also a part-time teacher in Data Science in a bootcamp. I have already taught more than 100 students on topics such as data science, machine & deep learning, SQL, Python, statistics, cloud...I spend most of my time sharing my knowledge and reading scientific articles in this field!I would be delighted to discuss with you.PS : I love tennis too!

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