good enough to review NLP materials

very good and simple explanation!

Great, but sometime many teorical

Grader issues, but generally good

A very nice and concise course.

The slides should be provided

you can add french subtitles

awesome content but too easy

Vector Modelling for NLP

Much recommended course!

Explained LSH very Well.

Bugs in Assignment code

오타나 설명 부족이 눈에 너무 보였다.

Good but very basic

Course is too easy.

well explained !!

really good

excellent

decent

nice!

Nice

Good

It's really hard to rate this course.

Pros:

- I think the coverage of the material in the lectures is excellent, and it does a good job of simply explaining some pretty complex topics.

- The instructors did a good job of pulling together real-word-relevant examples of applications, which made me feel more motivated to continue working on the material.

- The pacing is fast, but I didn't feel overwhelmed.

- There are nice visualizations

- The instructors are really friendly and enthusiastic.

Cons (serious, nearly crippling cons):

- The autograding tests are broken on at least one lesson, meaning that even someone who meets the specifications may lose points. Nobody seems to be in a hurry to fix these problems.

- The quality of the code is frequently ATROCIOUS. Whoever wrote the code failed to understand basic things like the fact that search time in dictionaries is O(1) and you don't need to use the keys() method to iterate through a dictionary. It's not all bad- there is plenty of reasonably well-written code in the course, as well as code that looks wonky but isn't really that bad. But there's also a lot of code that is very poorly written and inefficient.

- There's also a lack of consistent style in the code, which isn't wrong per se, but really makes the content harder to read. In particular, there should be no "'string1'+str(x) + 'string2'" syntax, which is a bad habit that I had to break a while back. f-strings are the way to go.

- To conclude, this is as SERIOUS PROBLEM because some students will be learning "good coding practices" from this course and others like it, and if they learn about some of the relevant Python libraries from this course, they may learn terrible habits from this course.

It's clear that the creators of this course could not decide who it was going to be aimed at, or what level it would be. So, you end up with a course that is too light on the NLP but assumes anyone doing the assignments knows the little numpy and dictionary tricks that they do. Consequently, the assignments do not test your understanding of NLP, only your understanding of how the notebook creators code.

The videos are far too short, a common complaint I can see from other reviews. Additionally, they fell into a common trap that plagues script writing for education. What you absolutely must not do, and is exactly what they do, is just machine gun through the information and terminology when presenting. If you watch good lecturers, they leave time for concepts to settle in and they will reinforce key points by restating them in a different way. They know how to hit the beats because they know how people learn. An information dump, as we have here, is a poor didactic method.

The assignments are mostly okay and use notebooks where you have to 'fill in the blanks'. There are some flaws with this, the first being that you have to write the code in the format they want, so alternative methods are marked as wrong. Even more severe is that there are insufficient checks in some of the later notebooks. It is possible to get far into one and obtain the expected results, only to have one cell give the wrong result. This means the error is in an earlier cell and you have no way of knowing where it is without spending a long time exploring. This problem is especially bad in the final week's assignment, which is overly long and has an insufficient number of checks.

This course has a lot of promise as an introduction to NLP methods. It does a clear job of introducing logistic regression, Naive Bayes, and basic concepts of embeddings. However, I have some significant reservations about the current state of the course.

First, the course is introduced by Andrew Ng as being taught by Younes Bensouda Mourri and Lukasz Kaiser, and heavily promoted by Andrew's marketing through deeplearning.ai and The Batch. In reality, Andrew is barely involved (except for a couple of excellent optional interviews), Lukasz says a sentence or two at the beginning of each lecture, and Younes handles the lectures. Younes is just fine as a teacher, but it is clear he is reading from scripts and one feels as if the course was advertised as being taught by a more senior team. It does not have anything like the feel of authority of Andrew's classic Machine Learning course on Coursera.

Second, there are various small errors in the materials. For example, one slide set that has numerous calculations wrong because a column of numbers is summed to 12 rather than 13, and the course has a small notice about the error rather than correcting the slides. There are various confusing instructions (and some small errors) in the programming assignments.

Third, some of the choices of content were odd. I did not understand why week 3 spent much of the programming assignment on the details of implementation of PCA (which is a visualization technique not an NLP technique), without really teaching the underlying math.

In sum, this is a good introduction to NLP concepts but as yet below the standard that one expects in the Andrew Ng universe.