Developing and Deploying Applications with Streamlit

🎉 Master Developing & Deploying Applications with Streamlit! 🚀

Course Title: The Fastest Way to Build and Share Data Apps with Streamlit

Headline: Elevate Your Data Science Projects into Interactive Web Applications with Streamlit - Effortlessly!

🌟 Course Description: Streamlit, the open-source app framework for Machine Learning and Data Science teams, is revolutionizing the way we create data applications. With Streamlit, you can transform your data scripts into fully functional web apps within minutes! It's all Python, free to use, and backed by a vibrant community.

Whether you're a seasoned data scientist or just starting out, this course will guide you through the entire process of developing and deploying applications using Streamlit. From setting up your environment with Anaconda to sharing your polished app on the web, we've got you covered.

What You Will Learn:

1. Environment Setup:

   • Installing Anaconda and creating a virtual environment.

2. Streamlit Installation & Dependencies:

   • Getting Streamlit, pytube, and firebase up and running in your environment.

3. GitHub Integration:

   • Setting up or integrating with your existing GitHub account.

4. Building Interactive Apps:

   • Displaying information and using widgets to enhance user interaction.
   • Working with data frames for loading and displaying data.

5. Creative Features:

   • Crafting a custom image filter inspired by Instagram.
   • Developing a YouTube video downloader using the pytube API.

6. Data Visualization:

   • Creating interactive plots with user-selected inputs and animated visualizations.

7. Multipage Apps:

   • Structuring, running, and adding pages to your app for a more complex and navigable experience.

8. Authentication & Security:

   • Implementing user authentication with Streamlit-Authenticator via both Pickle File and Database.

9. Natural Language Processing (NLP):

   • Building applications that utilize OCR for image to text conversion and integrating ChatGPT for auto reviews and Leetcode problem solving.

10. Additional Applications:

    • Creating a personal portfolio page, deploying with Streamlit Cloud, understanding sessions, and utilizing NTLK.
    • Working with SQLite databases, including connecting to and interacting with data.
    • Developing additional apps such as a static code quality analyzer and a NoSQL job board using Firebase API.
    • Converting machine learning models like Random Forest into Streamlit applications.

🔧 Content in Progress: Soon, we'll be adding more material including creating a personal portfolio page with Streamlit, deploying applications with Streamlit Cloud, exploring the concept of Sessions, integrating NTLK, and working with SQLite databases.

Join us on this journey to transform your data into engaging, interactive web applications that stand out in the world of data science and machine learning! 💻✨

Enroll Now & Start Your Journey with Streamlit!