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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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776 - 800 of 876 Reviews for Natural Language Processing with Classification and Vector Spaces

By Sophie C

Dec 21, 2020

Not sure of what I should master after this course. If it's the theory, or, say, the main principles, then OK : I have an idea of how NLP with vectors spaces works. But I feel totally unable to implement it, concretely. Perhaps it's not a problem if, in fact, we are just shown this technique as an initiation, before more complex ones.

By Kenny S

Feb 13, 2021

Overall, the course covers a good content and informative but it lacks of in-depth discussion of each topics of NLP. It's not as thorough as Deep Learning Course. Also, the programming assignment is too picky about which functions of Python to be used while there are several ways to achieve the same outputs.

By Aman S

Jul 12, 2021

You guys need to work on the programming assignments, especially the teaching is below par as u guys didnt differentiate the word embedding we found by word by doc model which has to be a whole number and the embedding matrix which we generate from -1 to 1 which captures relationship between words.

By Mark L

Aug 21, 2021

I would like to have seen more breadth and depth in the course, and of course I have my perpetual beef with certain Coursera courses like this one that grade the programming assignments by looking for code features (which must be matched exactly) rather than evaluating results.

By Tanmay R S

Oct 22, 2022

not enogh explanation of topics ... please give in detail explanation of topic . It seems like after this course i need to do few more courses on the same topic cause it just introduces to the concepts and not giving in depth knowledge like other courses of andrew ng.

By Christopher M

Aug 3, 2023

Great information but not enough opportunities to practice skills or internalize concepts. The assignments are too easy and don't let you flex much brain power. I feel like the lack of any repetition will result in almost immediately forgetting material.

By James M

Nov 6, 2021

I feel like feed back and testing of your code code be more detailed to help pin point coding mistakes. I was spinning my wheels at the end and did see any solutions or discussions on my issues. I still passed but would like to see what I did wrong.

By Phước T V

Sep 12, 2021

The lecture videos are a little short but provide some fundamental insights. It would be better if the videos were longer and more detailed or some supplemental resources. Overall a good course if you are a beginner or don't know where to start.

By Sherali O

Dec 25, 2020

Shallow explanation in some topics in the lectures. It would be great if lecturer explained topics in more detail, and answer questions like why we use this model, show how it was created, pros and cons, and show why it works using math proofs.

By Espoir M

Sep 15, 2020

I like the way the course use simple machine learning technic to solve a complicated problem,

for someone who likes mathematic a lot could be done in explaining mathematic concepts,

the assignment could be improved by using unit testing.

By Gianpaolo M

Jun 24, 2021

Andrew, come back with us!

Although very interesting, the course spend too many time and many student efforts in details like PCA and LSH. This is a good way to loss the big picture during the course.

By Sina M

May 14, 2023

Compared to prior deepLearning Ai courses. the lecturers were very robotic and un natural. The explanations were much less clear and less effort was made to explain the intuitons behind formulas.

By shaider s

Dec 19, 2020

Lectures were very straightforward and digestible, however the assignments had inconsistencies within themselves, especially between the written instructions and the comments in the code cells.

By Ketipisz V

Jan 3, 2022

It's a very high level overview, I was expecting a bit more detail. The programming exercises are very basic, it felt like there could have been less but more advanced challenges to solve.

By Benjamin W

Jul 19, 2024

Interesting, but surprisingly many quality issues. Some topics, such as naive Bayes classification, need a better motivation (explain intuition and connection with Bayes theorem first).

By Bogomil K

Jul 27, 2021

The topics were interesting overall and the lectures even though rather short were still rather informative. Too much focus on specificities of libraries and frameworks in the exams.

By Hamman S

Jan 13, 2021

While this was a great introductory course to some of the basic tenets in NLP, various ancedotal examples were too convoluted to be useful in gaining an intuitive understanding

By Mansi A

Aug 23, 2020

This course provides you with a good but basic start to the world of NLP. Week 4 LSH and Hashing should be explained more clearly. Assignments are not challenging.

By N N

Oct 7, 2020

Basically lecturers' delivery is not so good that you could get distracted easily.

Often, a video contents and a jupyter notebook don't match to each other.

By PRANSHU K

Sep 14, 2020

Seemed easy to me. Rest all is good, the explanation and assignments.

I am reducing star by one rating because of the interface for assignment is poor.

By Michele V

Sep 17, 2020

Good coding part. For my background the lecture material was a bit too easy. However, if your intention was to keep it easy, then good job!

By Yuthika B

Nov 30, 2022

The course misses depth and needs to focus on applications of these algorithms rather than introducing more and more algorithms so fast.

By Sebastian J

Mar 25, 2024

The videos were too short to properly explain things and the notes sections after each were basically just screenshots of the video.

By Toon P

Jun 7, 2022

It is rather annoying that the videos are short and even shorter because half of the time is spend on an intro and outro

By Diana G

Apr 30, 2024

The course has been beneficial, but it could greatly benefit from more thorough explanations of mathematical concepts.