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Download Developing and Deploying Applications with Streamlit

Download Developing and Deploying Applications with Streamlit

What you would learn in Developing and Deploying Applications with Streamlit course?

streamlit is an open-source application framework designed for Machine Learning and Data Science teams.

Streamlit lets you transform your data scripts into shareable Web applications within minutes. It's entirely Python open-source, open-source, and free! After you've built an app, you'll be able to use the cloud-based platform we offer to build and manage your app!

In this course, we'll discuss everything that you should know about streamlits, such as

  1. Installing Anaconda and then creating a virtual env

  2. Installing Streamlit , Pytube, and firebase

  3. The process of setting up a GitHub account, even if you do not have one

  4. Display Information by Streamlit

  5. Widgets that have Streamlit

  6. Data frames are used to work ( Loading, Displaying )

  7. Making an image filter ( we make use of the most popular Instagram filters)

  8. Making a YouTube video downloader (using the python API)

    1. Pytube is a light, free-of-dependency Python library used to download videos from the internet.

  9. Making Interactive plots

    1. The user has input the value of the chart

    2. animated plot

  10. A Brief Introduction to Multipage Applications

    1. Structure multipage apps

    2. Run a multipage app

    3. The addition of pages

  11. Create an OCR Image to Text conversion using the tesseract

  12. Content that is in the process to be released shortly

    1. Concept of Sessions

    2. NTLK with streamlit

    3. Making a personal portfolio page using a streamlit

    4. Install Application using Streamlit Cloud

    5. Working with SQLite

      1. Connecting to databases

      2. Accessing data from databases

      3. Writing Data into the database

    6. Additional Applications

      1. Quality analyzer for Static Code

      2. No SQL Job Board is using Firebase API

      3. Converting random forest models into streamlit applications

Course Content:

  • Streamlit and the benefits it offers.
  • Streamlit's features help create web, data, and machine learning applications.
  • Implementing streamlit applications on cloud-based streamlit
  • Personal Portfolio page hosted by the cloud that is streamlit

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