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

4.6
stars
4,441 ratings

About the Course

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....

Top reviews

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

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826 - 850 of 876 Reviews for Natural Language Processing with Classification and Vector Spaces

By jkf

Oct 14, 2020

Just ignore the video!

By Rishik R

Apr 5, 2021

Too easy

By Dmitriy I

Jan 28, 2021

Too easy

By Adam S

Dec 11, 2022

This class is disappointing, especially after the Machine Learning Specialization classes which were given by Andrew Ng. Overall, this is the kind of class where the detailed syllabus the most valuable component. There is good information and topical introductions here, but I think the lecturer has forgotten the feeling of not being 1000% familiar with the material, especially the math.

Some notes:

1) Most, if not all topics are glossed over very quickly, especially mathematical ones. I very much miss Andrew's deeper (and more extensive) "intuition" videos here, and I say that as someone with a degree in computer science.

2) There are many errors in the lectures and in the labs. Sometimes a "popup" will tell you about them, sometimes not.

3) Popup quizzes in the lecture videos happen before the lecturer has even finished speaking about the topic being quizzed and are very jarring.

4) The "practice" (but still graded) quizzes have a difficulty level way out of proportion to the lecture content, especially those mathematical concepts that are so quickly glossed over. If you really must spend only 30 seconds on an equation and then expect us to remember/work it off the top of our head in a quiz days later, give us some exercises or some practice at least!

5) Labs are mostly just literal steps of "type this", "now type this". When it is more demanding, the instructions are not very useful and do not prepare anyone for the quirks of how libraries like numpy work. In many cases, there is no way to explain or diagnose why my numerical outputs were different from expected, especially when I followed all the instructions. Having to deep dive into the numpy documentation to find arbitrary arguments based on (seemingly arbitrary) data structure choices of the lab author in order to complete a lab doesn't feel productive or motivating.

6) Although a minor nit, the audio inconsistency between talking head and slides is very jarring. Compare to any of Andrew Ng's videos!

By christ.hoff@gmx.de

Sep 3, 2020

I believe the course does not allow you to study NLP in depth. Compared to the deep learning specialisation by deeplearning.ai, this course has probably hours(!) of video material less. PCA is for instance presented in ~4 minutes and the lecturer concludes with "now that you know all about PCA". The only further reference provided is a link to the standard textbook in the field, no detailed study guide or references for individual topics. Excercises are done in notebooks and test beginner python skills instead of nlp understanding (Basically: "Look up key i in dictionary j and store vector k"). It does do a good job in giving an overview about NLP.

By Andreas B

Sep 8, 2020

I was torn between two and three stars. Two, as mathematics are dealt with far to shallow. No proofs, no motivation, nothing. And in the final week, there is a massive notebooks with a lot of flaws and a lot of cells you have to code in a specific, sometime suboptimal, way. Otherwise, the grader will throw errors. All in all, things are handled to shallow and it is more of a coding lesson than a deep dive into ML, which necessarily requires mathematics. This is one more of those "Become a data scientist without mathematics" things the world does not need.

By Shawn

Jan 29, 2022

lectures are pretty mediocure. basically it lacks motivation behind algorithms, you are simply told what to do, really like "machine" learning

you'll spend a lot of time in the assignment, not focusing on implementing your algorithm, but adjusting incorrect input or output format that passes all tests but fail the final grading for some reason (also in week 3 the assignment has one or two questions that do not even tell you what's the input data and you have to "print" them to get an idea lol)

By Jorge E P C

Feb 3, 2021

The lectures skip over important features that should be explained in more detail. Other important concepts are left to the labs, even if those require a good explanation. Evaluations are not a help to practice or understand concepts. Most of the time spent on evaluations is figuring out how to do things in Python rather than follow the concepts. People can obtain 100% in the evaluations but learn nothing. It is indeed a very poor course.

By Paul D

Oct 19, 2020

Rather shallow and elementary course, I am disappointed. I did the deep learning specialization so I expected the same quality level here, but no it's not the case. In a way you're exploiting the fame of Andrew Ng to attract students, but Andrew is not teaching here and you're offering a content that is not up to expectations. I will try the following course and hopefully it will be more interesting.

By Foteini D

Feb 23, 2021

The course covers some basic aspects of Natural Language Processing and can serve as an introduction to NLP, but it definitely not an in-depth course. The assignments are just some Python scripts where you have to fill in the gaps, which is not a helpful learning strategy, since you aren't given the opportunity to try things out by yourself and figure out what works.

By Darien S

Nov 18, 2020

There are too many errors in this course. The lecturing is uninspired. The assignments are boring and weak. Coming from many Andrew Ng courses and the Deep Learning Specialization, this is a huge disappointment. You had an opportunity to create an organization responsible for top tier tutorials and learning, and this level of commitment wastes that potential.

By Jaspreet S

Oct 28, 2021

This particular course was not as good as other DeepLearning.AI courses are. This one was a bit non-intuitive and a lot of not so real life useful modules. Would be more interested in implementaion of applications, rather than building from scratch, because in real life implmentaion, we all are going to use libraries only.

By Fabian Z

Sep 4, 2020

The content is pretty nice, but the videos are so superficial. The assignments provide more theoretical background than the input sessions. There are many mistakes in the slides as well as in the assignments. I feel that this course is not mature yet.

By Krithika R

Mar 17, 2021

The course runs through the important NLP topics at a very high level. It mostly explains the steps to solve problems using various methods, but an intuitive explanation of the techniques is missing.

By Yu Z

Mar 8, 2021

The concept explanation is too weak in videos. The homework is too much focus on coding. The overall course lacks interactions between teachers and students , but I do learn some new stuff.

By Tim L

Sep 16, 2020

Exercises can be completed without the need to understand the methods. No flexibility in solving the programming assignments. Many small inconsistencies in the notebooks.

By Shafquat M

Nov 6, 2020

The course is very hurried without going into much details. It would be better if the instructors spend more time on the theory rather than skimming through codes.

By Kabakov B

Sep 5, 2020

Too superficial. Without DL specialization it could be too tough to understand what is takes place here. With it, everything is just too simple and flat.

By Amit A

Aug 2, 2021

Giving 2 stars because of notes provided with lectures, but there is a huge difference between what is taught in lectures and the real implementation

By Victor S

Feb 10, 2021

Mostly elementary feel-good with dinner recipe style follow-ups. Assignments are not set up well, have weird issues with submission error margins

By Sahar N

Jul 3, 2022

Comparing it to other deeplearning.ai courses the content was harder to follow and the assignemnts were not super eucational.

By Daniel J

Aug 19, 2020

Might be good for absolute beginners but if you have some background in maths or machine learning you won't find much new.

By Tsegaye H

Jun 25, 2020

I want to skip this course because I don't enough background in the concept to be able to follow the lectures.

By Carlos M P

May 8, 2023

Not a great course, honestly. The videos are too shalow, and the exams involved completing functions.

By Asghar H

May 21, 2024

Gets very confusing after week 2. The grammar used by the instructor is not very clear