Build a Web Application with Python, Flask and NLP

Share the joy of famous quotes with a cloud-based web app using natural language processing to hit the right mood!
Rating: 4.3 out of 5 (127 ratings)
10,253 students
Build a Web Application with Python, Flask and NLP
Rating: 4.3 out of 5 (128 ratings)
10,253 students
Easily create web applications using Python and Flask
Quickly port your Python ideas to the web

Requirements

  • Basic knowledge of Python, HTML, and Jupyter notebooks
  • Ability to install Python libraries as required per course (pip install or however you do it on your OS)

Description

Let's share the wonderful joy of famous quotes to the world with a quoting machine web application that uses natural language sentiment to tailor the right quote for the user.

The class will teach you how to take your Python ideas and extend them to the web into real Web Applications so the world can enjoy your work.


In this class, we will:

  • develop our ideas in a local Jupyter notebook

  • gather data (famous quotes)

  • use the Vader NLP sentiment algorithm

  • tune our models and dispensing mechanisms locally

  • design the look and feel

  • get graphics

  • extend responsive HTML templates

  • port to the web using PythonAnywhere

  • enjoy great quotes in tune with our moods 24/7

Above all, you will understand how you can port your own Python ideas to the web into fully interactive web applications so the world can enjoy your work!

Who this course is for:

  • Anybody wanting to extend their programmatic reach
  • Anybody wanting to share their work to the entire world by porting to the web

Course content

1 section • 12 lectures • 1h 59m total length
  • Introduction
    03:30
  • Questions Before Starting Any Project/Software Journey
    08:31
  • Storyboarding
    04:32
  • Finding Data - Where to Find Famous Quotes
    08:28
  • Sentiment Scoring With the NLTK Vader Tool
    11:31
  • Building the Quote Dispensing Engine - Abstracting With Functions
    13:26
  • A Few Good Tools to Design a Web Application
    09:51
  • A Flask primer
    16:55
  • Getting Professional Help - How I Do It With UpWork
    10:29
  • Building Our Quoting Machine on PythonAnywhere - Part 1
    13:34
  • Building Our Quoting Machine on PythonAnywhere - Part 2
    13:18
  • Conclusion
    05:36

Instructor

Data Scientist & Quantitative Developer
Manuel Amunategui
  • 4.4 Instructor Rating
  • 1,099 Reviews
  • 31,041 Students
  • 12 Courses

Data scientist with over 20-years experience in the tech industry, MAs in Predictive Analytics and International Administration, author of Monetizing Machine Learning and The Little Book of Fundamental Indicators, founder of FastML, reached top 1% on Kaggle and awarded "Competitions Expert" title, taught over 20,000 students on Udemy and VP of Data Science at SpringML.


From consulting in machine learning, healthcare modeling, 6 years on Wall Street in the financial industry, and 4 years at Microsoft, I feel like I’ve seen it all. And this has opened my eyes to the huge gap in educational material on applied data science. Like I say:


"It just ain’t real 'til it reaches your customer’s plate"


I am a startup advisor and available for speaking engagements with companies and schools on topics around building and motivating data science teams, and all things applied to machine learning.


Reach me at amunategui@gmail.com