Complete Guide to Data Science Applications with Streamlit

Posted in: Tutorials | By: LeeAndro | 4-01-2021, 12:11 | 0 Comments
04
January
2021
Complete Guide to Data Science Applications with Streamlit
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 151 lectures (9h 45m) | Size: 4 GB

Analyzing data and building machine learning models is one thing.


Learn how to build and deploy data science applications in Python

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

Basic Python Programming, however a Python crash course is included

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

At the end of the course, you will have built several applications that you can include in your data science portfolio. You will also have a new skill to add to your resume.

The course also comes with a 30-day money-back guarantee. Enroll now and if you don't like it you will get your money back no questions asked.

Individuals interested in building data science and machine learning applications in Python



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