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Opiniones y comentarios de aprendices correspondientes a Natural Language Processing with Classification and Vector Spaces por parte de

3,105 calificaciones
633 reseña

Acerca del Curso

In Course 1 of the Natural Language Processing Specialization, 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. 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....

Principales reseñas

17 de jul. de 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.

24 de may. de 2021

Great Course,\n\nVery few courses where Algorithms like Knn, Logistic Regression, Naives Baye are implemented right from Scratch . and also it gives you thorough understanding of numpy and matplot.lib

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1 - 25 de 644 revisiones para Natural Language Processing with Classification and Vector Spaces

por Forest L

19 de jun. de 2020

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

por Anand R

19 de jun. de 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.

por Zhen L

21 de jun. de 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.

por Juan d L

25 de jun. de 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

por Sarvesh K

19 de jun. de 2020

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

por Oleh S

26 de jun. de 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.

por Agrita G

1 de jul. de 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

por Clement K

30 de jun. de 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)

por ES

25 de jun. de 2020

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

por Mohamed A H A M

25 de jul. de 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

por Dmitry Z

13 de jul. de 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].

por Sharan N

9 de jul. de 2020

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

por sukanya n

10 de jul. de 2020

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.

por Sathvik J

18 de jul. de 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.

por Owais A

17 de ago. de 2020

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.

por 01 H A

9 de ago. de 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

por Фридман Р Г

5 de jul. de 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.

por Chengzhi L

22 de jun. de 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!

por Miguel O

18 de jul. de 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...

por Anshul B

6 de feb. de 2021

Good explanations, covers some fundamental concepts in text classification and vector embeddings. Not a wholesome introduction to NLP and Text Analytics if that is what you are after

por Tanay G

4 de jul. de 2020

This is the first course that I found really boring, maybe because the material presented was quite superfluous.

por kk K

15 de jul. de 2020

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.

por Zachary B

7 de ago. de 2020

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.

por Mounir H

14 de sep. de 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.

por John J M

1 de ago. de 2020

Video lectures are short and concise. The basic ideas are well presented. Some references for the details of vector subspaces and spanning vectors would have filled out the mathematical framework.