Complete Guide to Data Science Applications with Streamlit

What you'll learn:

• Building Data Applications with Streamlit
• Integrating Matptlotlib & Seaborn in Streamlit
• Plotly Visualizations in Streamlit
• Authenticating Streamlit Applications
• Deploying Streamlit Applications
• Using Streamlit Components
• Altair Visualizations in Streamlit

Requirements:

• Basic Python Programming, however, a Python crash course is included

Description:

Analyzing data and building machine learning models is one thing. Packaging these analyses and models such that they are sharable is a different ball game altogether.

This course aims at teaching you the fastest and easiest way to build and share data applications using Streamlit. You don't need any experience in building front-end applications for this. Here are some of the things you can expect to cover in this course:

• Python Crash Course

• NumPy Crash Course

• Introduction to Streamlit

• Integrating Matplotlit and Seaborn in Streamlit

• Using Altair and Vega-Lite in Streamlit

• Understand all Streamlit Widgets

• Upload and Process Files

• Build an Image Processing Application

• Develop a Natural Language Processing Application

• Integrate Maps with Streamlit

• Implement Plotly Graphs

• Authenticate Your Applications

• Laying Out your Application in Streamlit

• Developing with Streamlit Components

• Deploying Data Applications

Who this course is for:

• Individuals interested in building data science and machine learning applications in Python
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Course Details:

• 9.5 hours on-demand video
• 4 articles
• 2 downloadable resources
• Full lifetime access
• Access on mobile and TV
• Certificate of completion

Complete Guide to Data Science Applications with Streamlit udemy courses free download

Learn how to build and deploy data science applications in Python