A truly great course, focuses on the details you need, at a good pace, building up the foundations needed before relying more heavily on libraries an abstractions (which I assume will follow).
A neatly organized course introducing the students to basics of Processing text data, learning word embedding and most importantly on how to interpret the word embedding. Great Job!!
By James P•
I found the course really helped to reinforce my understanding about importants concepts like n-grams, HMMs and word embeddings. The labs are pretty well spread out, and by the time you get to the week-ending assignments, you have all the info you need to complete.
By Will H•
The lectures on the Viterbi algorithm were a little wooden and there were no summary text (reading) tasks (as there often is in other deeplearning.ai courses), however this is a worthwile and informative course.
By Rafael C F d A•
In the first and second week the exercices have some unecessery pranks in the data formatation just to make the exercice harded, but it take out the attention for what matter in the course that is NLP
By German C M•
Very good to see how the "from scratch" concepts are presented; nevertheless, I have the feeling that a very little "real use case" problem has been presented, with tiny sentences being analyzed.
By Osama A O•
Good course, but the lecture notes in week 2 can be much more improved. Understanding Viterbi algorithm without visuals and animations was very difficult. Apart from that, great course!
By Ramprakash V•
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.
By Cheng J•
The Viterbi algorithm introduction is a bit hard for us to follow. Probably some writings may help guiding through each steps.
By Hernan J•
Esta especialización junto con la de Deep Learning se complementan y es son más claros los conceptos y prácticas, gracias!
By Zoutao W•
The tutor sometimes pass the slices too swiftly. I hope that he could wait 2-3 seconds after finishing speaking.
By Sandeep V•
Sone Quiz should also be there. Assignments can be solved by python knowledge an following the instruction
By Gopal M•
Assignments were incorrect.
Lot of content was squeezed in the last week. Even spread would be ideal
By Aung Z P•
I love the way the instructor teach and the course design which is made to be simple but effective
By Mounir H•
I didn't like weeks 1 and 2 too much but I liked week 3 and I really liked week 4.
I liked the lecture, very well prepared. Only the part on metrics was a bit short
By Vladimir V•
This is a good course but I would like to see more emphasis on the mathematics.
By Manuel V B•
Great course, but the last week felt a bit messy with submission evaluation.
By Sophie Z•
Not sure if it is on purpose, but W4 labs have repeating content.
By Jinsong T•
Too basic and going at too slow a pace
By Esakki p E m•
excellent Material & teaching
By AVIJIT J•
Good, very good.
By Randall K•
By Teresa M B•
* some of the content is well-explained
* provides good solid knowledge about the background and implementation of common NLP tasks
* notebooks (and content generally) are unevenly distributed
* significantly stronger focus on ML, rather than on the NL side (this is consistent throughout the specialization)
* some of the explanations (e.g. in week 2) aren't clear
* specialization could be structured better -- word embeddings are introduced in course 1, but the in-depth discussion is here in week 4; would perhaps have made more sense to have that content build on itself
By J N B P•
In this course, you will learn to build an autocorrect model and different methods of building this model. The course felt a bit rushed with a lack of detailed explanation, students who are familiar with the concepts of NLP from before starting this specialization won't face any problem, but students who had just begun learning NLP through this specialization might feel a little difficult.
By Amlan C•
Too many gaps in the course. Many concepts not covered in the mathematical sense basic Grad. Desc. math would have been helpful. Also if you want to omit it totally you should have atleast one lab on how one would do it in real life using which library? Pytorch? Keras? What? Rest of the course is okay. Younous is great in explanation.