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Learner Reviews & Feedback for Detect Fake News in Python with Tensorflow by Coursera Project Network

10 ratings

About the Course

"Fake News" is a word used to mean different things to different people. At its heart, we define "fake news" as any news stories which are false: the article itself is fabricated without verifiable evidence, citations or quotations. Often these stories may be lies and propaganda that is deliberately intended to confuse the viewer, or may be characterized as "click-bait" written for monetary incentives (the writer profits on the number of people who click on the story). In recent years, fake news stories have proliferated via social media, partially because they are so readily and widely spread online. Worse yet, Artificial Intelligence and natural language processing, or NLP, technology is ushering in an era of artificially-generated fake news. Both types of fake news are detectable with the use of NLP and deep learning. In this project, you will learn multiple computational methods of identifying and classifying Fake News. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....
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1 - 2 of 2 Reviews for Detect Fake News in Python with Tensorflow

By Charles I N I

Feb 16, 2021

Interesting topic! Good instruction.

By Teruki I

Feb 6, 2023

This was very hard to follow along, not necessarily because of the difficulty level of the content. Because this wasn't done in a Jupyter Notebook, I wouldn't realize that I had an error anywhere in my code until I ran the whole file. And I'd have to rerun it multiple times because you'd only get one error message at a time. This would've been better handled in Jupyter Notebook where you can run individual code blocks and more easily catch individual errors as you write up the code.

Also, in one of the videos, the instructor neglects to point out a lot of miscellaneous edits made to the code (he obviously cut out a few portions from the video where he made edits), so it became very confusing as I was trying to compare my code to his in order to debug.

I ended up running out of time because of these issues. I ended up spending way more time trying to debug my code rather than trying to understand what my code was doing.