SJ
Jul 17, 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.
MN
May 24, 2021
Great Course,
Very 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
By Zoizou A
•Oct 25, 2020
amazing
By Muhammad A B
•Oct 1, 2020
perfect
By Mohamed S
•Sep 8, 2020
PERFECT
By beomseok l
•Jan 8, 2024
Great!
By WLSC
•Mar 1, 2023
great!
By Thành H Đ T
•Oct 14, 2021
thanks
By Prateek S P
•Jan 17, 2021
thanks
By Jeff D
•Nov 8, 2020
Thanks
By Rafael C F d A
•Sep 28, 2020
Great!
By Kamlesh C
•Aug 30, 2020
Thanks
By Qamar A
•Aug 5, 2020
Cool!!
By ilham k
•Aug 16, 2023
bagus
By Mahesh
•Apr 17, 2023
fghrt
By Hemchand C
•Mar 11, 2023
.....
By B21DCCN436 N Q H
•Feb 14, 2023
grate
By Prins K
•Jul 28, 2021
Great
By 克軒廖
•Feb 5, 2021
Nice!
By Efstathios C
•Jul 16, 2024
Good
By 刘世壮
•Dec 4, 2021
good
By GANNA H
•Aug 4, 2021
good
By Ranjeet K
•Mar 14, 2023
no
By Abhinav S
•May 2, 2022
bk
By Dave J
•Jan 1, 2021
Having previously completed the Deep Learning Specialization, I came to this course with the intention of completing the whole NLP specialization, rather than because I was especially interested in the content of this first course from that specialization.
The Deep Learning Specialization sets a high standard of teaching quality and I have to say I found this course is not quite to the same standard. It's pretty good but not as good. The instructors are very knowledgeable, they make the effort to explain each topic clearly and they do a pretty good job of that.
What I felt could be improved is providing context of where each topic fits into the broader picture of both the theory and current practice of NLP. I was often left feeling, why are we spending time on this particular topic? Is this technique used in current practice or is it just of didactic or historical interest? Great teachers always have the broader context in mind and make sure that students see how everything fits into the bigger picture and why it is worth studying.
Although techniques were clearly explained, I felt that the underlying concepts were sometimes less well explained. An example is vector representations of words: we were shown the use of vector arithmetic to find analogies, but without much in the way of explanation of how this is possible. To me, this was the wrong way around: it makes more sense to me to first build an understanding of the representations, then introduce the remarkable result that these representations allow finding analogies.
In this course, sentences are represented as a "bag of words". This is processing natural language in the way a food processor processes food: chopping it up into a word soup. Since one of the most fundamental aspects of language is its structure, this might seem a hopeless approach. However it gives surprisingly good results for some simple tasks such as classifying tweets as having positive or negative sentiment. If you've done course 5 of the Deep Learning Specialization (Sequence Models), this will feel like a step backwards. There's no deep learning in this course. But I signed up for the course knowing that, so I can't criticise it on that basis. I'm taking the view that this course lays the foundations for more advanced and current topics in the subsequent courses in the specialization and I look forward to getting onto those.
The labs and assignments generally work smoothly. There are a few inconsistencies and a couple of the hints were a bit misleading but generally OK. It's a bit paint-by-numbers though, filling in bits of code within functions rather than working out for yourself how to structure the code.
By Kaiquan M
•Jan 22, 2022
This "Natural Language Processing with Classification and Vector Spaces" course covers: - Logistic regression - Laplacian smoothing, log likelihood, naive bayes models to predict sentiment of tweets - Euclidean distance, cosine similarity between word vectors to understand relationship between sets of text, and Principal Component Analysis - Language translation using rotation matrices, k-nearest neighbours and locality sensitive hashing The course has weekly lecture videos and has a summary reading after almost every video, which was especially helpful when trying to understand the concepts discussed in a video as a whole. There are also shorter labs to familiarise you with NLP concepts before the weekly graded programming assignment. Be sure to walk through and understand how the functions in utils_%.py accompanying each lab work. Similarly, walk through the functions in utils_%.py and how unit test cases are prepared in unittest.py accompanying each assignment. A good part of this course has been that the course team periodically releases new versions of the labs and assignments containing fixes or new approaches. Therefore bugs discovered by users in your assignment 3 months ago could already be fixed by the time you work on your assignment. The downside to the course is that the discussion forums were not actively monitored. Therefore there are some questions I have on certain concepts which were not answered by the time I completed the course.