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Learner Reviews & Feedback for Natural Language Processing with Classification and Vector Spaces by

1,317 ratings
303 reviews

About the Course

In Course 1 of the Natural Language Processing Specialization, offered by, you will: a) Perform sentiment analysis of tweets using logistic regression and then naïve Bayes, b) Use vector space models to discover relationships between words and use PCA to reduce the dimensionality of the vector space and visualize those relationships, and c) Write a simple English to French translation algorithm using pre-computed word embeddings and locality sensitive hashing to relate words via approximate k-nearest neighbor search. Please make sure that you’re comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text, and even built a chatbot! 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....

Top reviews


Jul 18, 2020

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.


Aug 09, 2020

one of the Best course that i had attented in the last week assignment was\n\nto good to solve which cover up all which we studied in entire course waiting for course 4 of nlp eagerly

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1 - 25 of 304 Reviews for Natural Language Processing with Classification and Vector Spaces

By Lin X

Jun 19, 2020

Lectures are too short and the topics are overly simplified. Assignments are toy examples.

By Anand R

Jun 19, 2020

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.

By Zhen L

Jun 21, 2020

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.

By Sarvesh K

Jun 19, 2020

i would have liked if the week 4's LSH and Approximate Hashing was explained more clearly.

By Juan d L

Jun 25, 2020

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

By Oleh S

Jun 26, 2020

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 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.

By Agrita G

Jul 01, 2020

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


Jun 25, 2020

The content is interesting. However, the assignments are too simple - the majority of the code is already written which defeats the purpose.

By Clement K

Jun 30, 2020

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)

By Sharan N

Jul 09, 2020

not worth it. the content is not related to the latest deep learning methods

By Фридман Р Г

Jul 05, 2020

Pretty simple course with some basic concepts superficially explained. Good feedback and help system through Slack channels for all weeks in the course. Although it fails to provide a deep understanding of the concepts it is trying to present.

By Chengzhi L

Jun 22, 2020

A fair level of difficulty for people with no background in NLP. The assignments are carefully designed to help the student to understand what he/she is doing. Looking forward to the next course!

By Miguel O

Jul 18, 2020

I came in with high expectations based on prior experience taking Andrews Ng's Marchine Learning course and Deep Learning specialization. Unfortunately, this course did not come close to meeting my expectations. The quality of the lectures is generally rather poor. The only real purpose they serve is to introduce terminology so that the student can seek better lecture material elsewhere. Some of the assignments may be interesting to folks with no prior NLP experience, but most are pretty basic to call this an intermediate level course. I recommend that folks fast-forward through the course lectures and find much better material available on youtube. Overall, I am pretty disappointed...

By Mohamed A H A M

Jul 25, 2020

Lacks depth and reading material

Still the same as all the recent watered down MOOCS, I miss the deep courses that resemble university courses

By Dmitry Z

Jul 13, 2020

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].

By Mounir H

Sep 14, 2020

Well paced and easy to follow.

There are some typos here and there (so the course might need some more polish on that end) but, apart from that, it's accessible and puts the focus on understanding the concepts rather on coding contrary to what I have read in another review.

You could follow the course even with no prior experience in Python.

If you take the course, don't skip the ungraded assignements, they are an integral part of it and provide more detailed explanations of what has been taught in the lecture videos.

Thanks to the team and good luck all.

By Harsh A

Aug 09, 2020

one of the Best course that i had attented in 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

By Dustin Z

Aug 02, 2020

A really great course in NLP. They do a really good job balancing beginner and intermediate skill levels. This is a good introduction to NLP and machine learning in general. Really fun course!

By Paul S

Jul 09, 2020

Fun, interesting and useful course. A couple of road bumps in the assignments made me waste a lot of time, but the forums and Slack channels were lifesavers in those situations.

By Carlos O

Jun 29, 2020

I has the right mix of challenge and support. I gained new insights into topics that I thought I already understood well. Great introductory course.

By alfredo m

Jun 25, 2020

Very helping in understanding the maths behind NLP for classification methods and I can see these things more intuitively from now on

By Dave W B

Jun 22, 2020

Good job! The course material is easy to follow and the links to related material is appreciated.

By Aleksander M

Jun 22, 2020

Great course, very good materials and explanations! ❤

By Robert S

Jul 24, 2020

General Comments on Course 1All of the linguistic and semantic knowledge that we were mining in Course 1 was encoded in the vectors. The coding was just using statistical methods to draw linguistic inferences from these vectors. I find it unfortunate that we didn't have an opportunity to learn how the vectors themselves are made (or did I miss something?), but merely got them out of a can from Google.It took me a while to figure out that the homeworks are graded by an AI, not a TA, and that one can submit the homework assignments numerous times until getting the grade you want. Given the large number of students enrolled, I can understand that hand grading would not be an option. It would probably be helpful to explain this to newcomers like me.I liked the way the assignments are structured. the fill-in-the-blanks approach, followed by some sort of numerical unit test to let us know if our solution was correct is good pedagogy (androgogy?). My only criticism is that sometimes the unit tests are not very sensitive to common coding errors. But now I know that we always have the option of running our assignment through the autograder for more complete feedback.The autograder is often overly prescriptive. For example, "Function 'np.sign or np.heaviside' not found in Code Cell UNQ_C17." Python is a rich language and there are many ways to code C17 without using those particular functions. The goal should be to get students to solve a problem creatively -- not to follow a particular path.I found the week 4 assignment a real bear -- too long (22 completion sections) in comparison with the others. I'm sure it was difficult for whoever codes the autograder as well. They need to do some more code checking. In UNQ__C8 we were asked to use the pre-coded cosine_similarity function. Initially this was returning the cosine difference, which is quite the opposite. In the middle of last week, after I and others reported the error, it was corrected but this seems to have created a cascade of other errors. For example, in cell UNQ_C9, We are toldExpected Output:[[9 9 9] [1 0 5] [2 0 1]] Which is wrong. A look at the vectors is enough to see that (2,0,1) is closest to (1,0,1) by cosine similarity. I believe the autograder makes the same confusion. I mention this not to criticize our hard-working programmer, who is otherwise doing an excellent job, but so that the errors get fixed ;-) (edited)

By Justin M

Jul 17, 2020

A high quality course overall! It helped me understand both theory and the programming mechanics of implementation. The Jupyter notebook guidance was detailed and well-organized!

Enhancement opportunities:

I felt the PCA lectures and PCA function implementation were a bit muddled. Consider illustrating the geometric intuition behind PCA: when 2-D data points are projected onto a line, the "best" line maximizes variance along the line while minimizing the reconstruction error of the data points.

The final notebook assignment is long and contains a large number of function and global variables. It is a lot to digest. Maybe enhance it with a takeaway video that unlocks after the assignment is passed. The video will visually recap what was accomplished by showing the start-to-end pipeline.

The course is a great value for the price!