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

4.6
stars
4,547 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. 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

YB

Oct 16, 2022

This course is excellent and is well-organized​. I would definitely recommend it to others. The instructor​ explains the topic in a crystal clear way​. I​ learned a lot and had a great time. Thanks!

MR

Feb 12, 2023

I really enjoy and this course is exactly what I expect. It covers both practical and conceptual aspects greatly and I recommend everyone to enroll in this course to make their NLP foundations strong

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676 - 700 of 899 Reviews for Natural Language Processing with Classification and Vector Spaces

By Nicholas Z

Mar 28, 2022

I think this course is a good complement to other AI/ML courses from DeepLearning.AI, but previous courses are definitely recommended to get the most benefit out of this.

By Vincent R

Jan 4, 2022

Good content, well-produced videos. Assignments are a little weird at times, but overall a good balance of showing what you've learned and not having to do all the work.

By Sofy Z

Nov 1, 2021

Well-build course, but as for me is quite superficial, I love deeper approach in teaching how things work and why. Although practical skills have definitely increased!

By Achuthan S

Oct 13, 2021

This is a beginner level, simple course for someone who is very new to ML/NLP; not challenging. Assignments are okay - the last one is the most useful/interesting IMO.

By Alok a

Aug 14, 2020

It feels me great to learn , via online media in this time of pandmic and yes of course the content quality is good , and so will continue to go for all the sessions.

By Abhijit D

Jun 4, 2021

Though i felt last assignment was pretty tough adn i had to seek lot of online material but overall vert nice course and gives introduction to nlp in very nice way!

By Aung K H

Feb 3, 2021

Great content. It would be nicer if the lecture videos spend more time going into the concepts. Lab and Assignment notebooks however, cover the concepts in detail.

By Paradorn B

Aug 15, 2020

Content is well compiled. A lot of theory and practice There should be a programming foundation. And linear algebra It will help you understand the lessons faster.

By Vadim S

Jul 31, 2020

I was sitting most of the time trying to reconnect to notebook. I don't know if this is the course fault or coursera's, but it exists. The content itself is good

By Błażej M

Sep 10, 2020

Great course! What I'd really like to see more is how the embedding database is build (it was mentioned how it might be done but there was no exact explanation)

By Brian M

Sep 14, 2020

Videos were very basic (and short), but the workshops and assignments were thorough yet well commented (in code) allowing for quick progress and learning.

By Gopal T

Mar 21, 2025

It was informative and a lot of learnings but the length of the videos explaining the concepts is too less as it need more detailed explanation.

By Ravi V K

Jul 20, 2020

I loved it overall! here are some considers...some more video explanations and references would have made this more interactive and game changer

By Swapnadeep S

Jul 17, 2020

Its an awesome course, but it would be nicer if students can learn to code on practical projects instead of writing everything just from scratch

By Akshay S

Aug 14, 2021

Nice content and easy explanation. There are a few mistakes in the programming assignments which should be corrected. Overall liked the course!

By Andrea D

Oct 5, 2021

Exceptionally well conduceted course, but I got to say that the last two weeks are weaker than the first two in terms of depth of explanation.

By Andrés M C

May 18, 2021

The way the course is evaluated could be different, because it is too literal and sometimes you get to the same answer doing different things.

By Randall K

Apr 2, 2021

I thought the HW was a bit too easy. I understand this is MOOC, but perhaps some optional assignments that don't have as much templated code.

By vijaya k e

Jan 17, 2022

Overall, the course is good. But, the last assignment of using KNN means with LSH is a bit difficult to understand. That needs improvemnt.

By Huziel E S F

Aug 13, 2021

In general good. The only problem was that some notebooks have typos, which makes the exercises a bit confusing. In particular in week 4.

By Abhinav G

Aug 7, 2020

The course content was well planned and assignments were good. But due to several errors in videos and grader issues, giving a star less.

By Vincent H

Jan 1, 2022

Course content is good, however, there are a number of bugs and errors in the quizzes and assignments that may throw people off.

By Sharthak G

Aug 6, 2023

I think the intuition for PCA should have been expanded a bit more, but overall a very good introduction to the specialization.

By Fabio

Oct 27, 2022

Great course, but it will be better if the instructors add a few more practical examples and explain a bit more the topics.

By Yuhao W

Aug 16, 2020

Last section LSH is a bit difficult, please add more details and extend current videos length to make LSH understood better.