Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio. By the end of this Specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future. This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
VG
Best Course Content for begginers as this course begins code with Scratch and popluates code with basic operations. In my opinion, this is the best course for NLP. Thanks Instructors
RV
The course is exceptional in its own way by bringing people to the understanding of probabilistic models. Crisp & Clear. But one need to explore & practise more to gain expertise.
BS
Great Course as usual. Tried siamese models but got a very different results. Will need to study more on the conceptual side and implementation behind them. But overall, I am glad I touched LSTMs.
CM
The lectures need more insights to understand not only the 'how' but a reasonable amount of the 'why', too. Andrew is very good at doing this in his lectures and provide his intuitions and insights.
ST
Ok but coding exercises could have been better structured (e.g. less long functions without easy to run test cases). The exercises could also have been a little more stimulating.
BN
This is one of the best courses i have taken. I have learned a lot from this course. Assignments were great and challenging. Thank you deeplearning.ai team for this amazing course.
CR
I wish the neural networks would be described in greater detail. Everything else is really nice, Younes explains very well. Assignments are very nicely prepared.
CR
It's a great course, more difficult than I thought but very well structured and explained. Although more didactic free videos can complete the lessons from others websites.
SR
Great introduction all fundamental concepts of NLP. Even though this is the easiest of all four in the specialization, it builds the solid theoretical foundation required elsewhere.
AL
Course well structured. SBOW very well explained and registered firmly. Word embeddings explained very well. Overall very happy from the learning’s
SB
amazing course. material is very well presented and explained! really loved the data generator part of the code - really drilled in the importance of it!
OM
This is a very recommendable course to understand state-of-the-art NLP techniques and models using neural networks.
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Lectures are too short and the topics are overly simplified. Assignments are toy examples.
This course seemed rushed, and navigated across depth and breadth very unsystematically. There were errors in the assignments and instructions, and the python code in the assignments was also very non-pythonic in many places.
I came to this specialization from Andrew's twitter post, wanted to give it a try since Andrew's DL specialization is very good. However, this course is not taught by Andrew, and the video lectures sound like the instructor is just reading the script, not really inspiring me to follow the lecture since the videos are very dry.
The course is interesting, and it is built carefully in all the aspects (videos and code), even though I followed it in their first days of live.
Grading process is more on programing than on understanding NLP (classification and vector spaces). For instance; Slight changes in the code (spaces, repeated codes of questions identification, lack of use of the proposed functions...) derive on fail to pass. Error information are frequently uninformative. It is not possible to check part of the code (see W4)
The use of "Slack" is not proper; information it is not easily accessible, unorganized and it demands on students the learning of new tools and additional payments.
Definitively I used more time with the code than with the NLP content
Hopefully this comment is useful for students and teachers.
Thanks a lot
This course has too many problems as it stands:
1) They haven't chosen an audience: the concept that they explain are trivial for anyone having (even basic) machine learning (or even basic linear algebra) knowledge. However, it doesn't meant that this explanations would be useful for beginners: they are too short and incomplete (the "videos" are on average 3 minutes long!!) and what they focus on is often not the most relevant part.
2) There is no reading material: no books, no papers, no theory. It wouldn't be a problem if the videos themselves were decent, but most of them are about 1 minute long. You can't explain machine learning in such a short time.
3) The code of the assignments, especially assignment 4, is unclean (e.g. unused variables) and contains minor bugs.
4) The script that grades the assignments has very strict requirements: as an example, very often, if you use x.dot(A) instead of np(x,A), then it complains and says you've failed. This happens for a lot of numpy functions, and it makes the process of submitting results tedious.
5) Again, regarding the course material itself, many of the key aspects are not discussed. For example, word embeddings are given that have some nice properties, but its never explained how they have been obtained.
Overall, it seems completely rushed.
i would have liked if the week 4's LSH and Approximate Hashing was explained more clearly.
Quiet good starting course for those who decided to study NLP. Materials are qualitative, but also too short. The course lacks of depth, lectures are too simple, hence in order to deepen knowledge and understanding one have to read a lot of additional resources, which are not provided here. I have ambiguous impressions about this course. Seems, the best DeepLearning.ai courses are those taught by Prof. Andrew Ng.
To sum up, I think, lectures duration should be increased and more deep intuition should be provided. Programming assignments are peace of cake for experienced programmer, but are OK for beginners. Also, there are many incomprehensible mistakes in programming tasks, which I suppose will be fixed later. Nevertheless, I recommend this course for those who want to start a journey to NLP world.
The course is interesting and useful, however I have to admit that I was expecting more. More and in-depth lectures, more tests, more coding. Sort of felt that currently it is too easy to pass all of the assignments and get the certificate without actually understanding concepts thought in the course
The content is interesting. However, the assignments are too simple - the majority of the code is already written which defeats the purpose.
A bit too easy, I wouldn't say no to more of mathematical formalism so that it does not cover just the tip of the iceberg (especially for LSH)
I have to say I was pretty disappointed with this course. I think there are two main issues. 1) The choices about what to dive deep on were not helpful. I don't feel like I have a high level understanding of most of the topics covered. 2) The assignments were not helpful in furthering understanding. I hope the next courses in this sequence are better.
The auto grader is ridiculous - e.g., in insisting that np.sum(X) is used instead of X.sum() [this is just one of many style examples].
Pros: Good amount of subject coverage and many tips and useful demo notebooks.
Cons: Sometimes it feels like one has to guess sometimes what they grader wants in the exercise at other times it feels like spoon-feeding. There can be better balance and error output explanations. More test cases for the assignments are needed to verify intermediate function outputs. Also, sometimes the course instructor mentions that you do not have to understand this concept or you can easily look it up online. This is not very encouraging because of course, one can look things up on the internet. It would be better suited to explain things however briefly it may be. Cue from Andrew's course "Neural Networks and Deep Learning", Andrew always explains the things even if it is brief and always gives you the intuition behind things.
All in all, recommended course to get started.
Lacks depth and reading material
Still the same as all the recent watered down MOOCS, I miss the deep courses that resemble university courses
This is the first deeplearning.ai course that I found really boring, maybe because the material presented was quite superfluous.
Many of the assignments had codes (like functions and concepts) which were explained in the lectures after the assignments. This was not good. Also, the videos were very short and unsatisfactory. Need a longer duration with some detailed explanation.
Awesome. The lecture are very exciting and detailed, though little hard and too straight forward sometimes, but Youtube helped in Regression models. Other then that, I was very informative and fun.
one of the Best course that i had attented in deeplearnig.ai the last week assignment was
to good to solve which cover up all which we studied in entire course waiting for course 4 of nlp eagerly
not worth it. the content is not related to the latest deep learning methods
One of the best introductions to the fundamentals of NLP. It's not just deep learning, fundamentals are really important to know how things evolved over time. Literally the best NLP introduction ever.