Natural Language Processing

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Below are the top discussions from Reddit that mention this online Coursera specialization from DeepLearning.AI.

Offered by DeepLearning.AI. Break into NLP. Master cutting-edge NLP techniques through four hands-on courses! Updated with the latest ... Enroll for free.

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Taught by
Younes Bensouda Mourri
Instructor
and 2 more instructors

Offered by
DeepLearning.AI

This specialization includes these 3 courses.

Reddit Posts and Comments

0 posts • 16 mentions • top 11 shown below

r/LanguageTechnology • comment
3 points • masters-in-phd
r/artificial • comment
1 points • justameremortal

u/t3tra__ https://www.coursera.org/specializations/natural-language-processing?utm_source=deeplearningai&utm_medium=institutions&utm_content=NLP_6/17_ppt

r/datascience • comment
1 points • WhipsAndMarkovChains

Go with what you're interested in! Here's an NLP specialization from Coursera and deeplearning.ai (I haven't taken it): https://www.coursera.org/specializations/natural-language-processing

r/MachineLearning • comment
1 points • vblagoje

You practically described Coursera NLP specialization.

r/QAnonCasualties • comment
1 points • Semesto

Cool video, thanks for posting!

One hack of a project though. I bet it could work to an extent but would fire off a high false positives rate because dog whistles are inherently hard to pick up on, especially for NLP. The model would require considerable further training as the alt-right expands on crap too. Said you don't have time to do it, but I've heard good reviews on this NLP class if you're at all interested in seeing how it might work.

r/datascience • comment
1 points • monkeyunited

You can start here: https://www.coursera.org/specializations/natural-language-processing#about and work your way back to close any knowledge gap (such as programming skills).

It's hard for any school right now to have a dedicated track for NLP due to how fast and how much things has changed.

For example, BERT came out in 2019 and was so powerful that the NLP world was basically split into pre-BERT and post-BERT days. In other words, any curriculum designed 2 years ago needs to be updated. Kim's paper came out in 2017 that showed you can apply CNN to sentences so if your program was designed 4 years ago and does not have a neural network component, it's already outdated.

r/learnmachinelearning • comment
1 points • matiu2

This is the video I initially saw on youtube: https://www.youtube.com/watch?v=quoGRI-1l0A

that made me re-sign up for the Deep learning Specialisation: https://www.coursera.org/specializations/deep-learning

In like 2017 I did everything in that except the last week (Sequence models). Thinking that they had added attention models, I re-signed up for it, but couldn't find them in there so cancelled.

This is the course I'm doing now: https://www.coursera.org/specializations/natural-language-processing also from deeplearning.ai

I just finished the first cert (4 weeks).

r/learnmachinelearning • comment
1 points • nuclear_pl

The answer to your question depends on the level of your ML knowledge. If you are familiar with basic ML concepts like cost function, optimization, basic properties of NN, you can start reviewing the Coursera course on NLP, DeepMind lecture 7, NYU course (week 6 and 7), and then you can take a look at the hugginface repo containing several transformer-based models.

r/linguistics • comment
1 points • kingkayvee

Speech and Language Processing by Jurafsky and Martin is often the introductory text to computational linguistics. Some resources and courses that support it:

https://web.stanford.edu/~jurafsky/NLPCourseraSlides.html

http://cs224d.stanford.edu/

https://www.cs.jhu.edu/~jason/465/

https://www.coursera.org/specializations/natural-language-processing

This isn't my area of specialization, but I believe you'd find good references in these resources and textbook to get an initial understanding.

r/compling • comment
1 points • korlmarcus

I'm a "Computational Linguist" at Amazon & I would recommend that you spend some time thinking about what areas you're most interested in, what problems you're interested in solving, what burning questions about language you want answered, what kind of company you're interested in working at, etc.

Then once you narrow your scope, start just googling those keywords and look up people/papers that are doing those things currently and figure out what kinds of technology they use.

Some other suggestions:

  • Google "computational linguist jobs" and read through job descriptions. It will go a long way to let you know what the "industry standard" for a computational linguist is
  • Look at syllabi for current MA/PHD programs in CL. They might point you to books and concepts that you want to learn more about. Some of them might have slides and resources directly on their program pages
  • Look at what people are presenting at conferences and see what grabs your attention. For example, I went to NAACL in 2019 and there were really cool presentations about building ML models to find political leanings of blogs and news sources. cool applications of nlp i hadnt thought of
  • Look into coursera udemy courses for NLP. Things like: Udacity NLP Nanodegree & Coursera: NLP Specialization

For books & projects, I think the other commenter's paper list will help a lot but these are the books that I've worked through as I was developing my skills: https://web.stanford.edu/\~jurafsky/slp3/ & https://www.nltk.org/book/

Hope this helps! Ultimately you'll need to do the work to figure out what you want to work on and learn. It's gonna take years to develop the skills but there are so many interesting problems that can be solved with the skills of a CL, so i wish you luck

r/learnmachinelearning • comment
1 points • Sagyam

Coursera is giving lot's of good courses with certifications for free right now if you have a student email ID. All of these are in coursera

  1. Deep Learning Specialization from deeplearning.ai
  2. TensorFlow in Practice Specialization from deeplearning.ai
  3. Natural Language Processing Specialization from deeplearning.ai
  4. Getting Started with AWS Machine Learning from AWS (There's no coding assingments and it teaches you about AWS APIs for ML/DL task)
  5. And obviously Andrew Ng Stanford Machine Learning Course