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Natural Language Processing with Classification and Vector Spaces に戻る による Natural Language Processing with Classification and Vector Spaces の受講者のレビューおよびフィードバック



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....




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.



Great Course,\n\nVery 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


Natural Language Processing with Classification and Vector Spaces: 526 - 550 / 703 レビュー

by Jim C


The material was a little shallow in places, and there are some long standing issues with assignments and quizzes that remain unresolved. Other than that, it was an interesting course.

by Deleted A


This is a quick and effective way to learn the basics of NLP. The course requires some understanding of linear algebra and calculus. The difficulty of the exercises is just right.



EXCELENT MATERIAL AND VERY CLEAR!! All the lessons and videos explained in a great way the exercises and theory. Just the final assigment was tricky and sometimes confussing.

by Varad P


This is a very good introductory course, unfortunately, it lacks depth. The rest of the things are amazing, as expected of, it was an enjoyable experience :)

by Nicholas Z


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


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 Sofya Z


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


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


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


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


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


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


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


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


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 Ravi V K


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


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


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


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


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


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


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

by Huziel E S


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


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


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