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Learner Reviews & Feedback for Introduction to Natural Language Processing in Python by Coursera Project Network

3.6
stars
28 ratings
7 reviews

About the Course

In this 1-hour long project-based course, you will learn basic principles of Natural Language Processing, or NLP. NLP refers to a group of methods for parsing and extracting meaning from human language. In this course, we'll explore the basics of NLP as well as detail the workflow pipeline for NLP and define the three basic approaches to NLP tasks. You'll get the chance to go hands on with a variety of methods for coding NLP tasks ranging from stemming and chunking, Named Entity Recognition, lemmatization, and other tokenization methods. You'll be introduced to open-source libraries such as NLTK, spaCy, Gensim, Pattern, and TextBlob. By the end of this course, you will feel more acquainted with the basics of the NLP workflow and will be ready to begin experimenting and prepare for production-level NLP application coding. I would encourage learners to experiment with the tools and methods discussed in this course. The learner is highly encouraged to experiment beyond the scope of the course. 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 - 7 of 7 Reviews for Introduction to Natural Language Processing in Python

By Utkarsh N

Aug 25, 2020

Wrong Code Snippets and very poor explanation!

By Joao P P

Aug 30, 2020

Subjects are interesting, but the whole project needs to be revamped. It does not seem like a real project, but like a cursory mini-course with miscellaneous information gathered haphazardly from the web.

By MD. M H

Sep 19, 2020

Script missing, no clear explanation

By Nguyễn H A D

Sep 26, 2020

I'm going to re-post my feedback from the end of the project survey.

Lingering question: Why did I do this project despite having read the negative reviews? (asked with sarcasm mixed with disappointment).

Confusing parts of the project: All of them, because the amount of explanation is close to non-existent.

Improvements:

1. Get rid of rhyme. Rhyme is a terrible tool, despite its good intention. While I was doing the project, Rhyme always forces the recording to be viewed in expand mode (or full screen) with 110% zoom in. This causes the recording to overlap my working screen, preventing me from simultaneously following the instructions. Also, the recording is forced to stopped whenever I focus on another window, preventing me from taking notes while listening the instructor. In general, it is unpolished and unproductive to use Rhyme.

2. The instructor needs to explain the concepts that are introduced, even a brief explanation is more than enough. Instead, all he did was reading of his script and presenting the codes without explaining or even reexamining his output. I (and possibly a lot of other students) even found some errors in his work, which is a lot considering this is a small project.

3. The resource used for this course is very poor. The sample text only has three lines with a couple repetitive words. There are a lot of existing datasets that can be freely used, which is better and more appropriate use.

n. There are a lot more, but I think it's better to take this course down and reevaluate this more appropriately.

By Simon S R

Sep 04, 2020

The instructor makes several mistakes and does not even see that his code does not what it should. I urge you to try out any other free tutorial instead, e.g. https://www.analyticsvidhya.com/blog/2020/05/what-is-tokenization-nlp/ .

Do not waste your time on this shoddy 'project'.

By Jonathan P

Sep 18, 2020

Good if you already know a substantial amount of NLP to begin with. If you like long-winded, jargon-dense info on NLP monotonously read to you then you will love this course. My favourite parts are where the code is just copy-pasted without any explanation.

By Misha C

Oct 05, 2020

Please don't waste your 10 bucks on this.