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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,

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


Natural Language Processing with Classification and Vector Spaces: 651 - 675 / 725 レビュー

by James M


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


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


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


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


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 Sikander H


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


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 Bogomil K


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 W S


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


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 non


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.



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


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 Toon P


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 Leonardo F R


Liked the in-depth linear algebra and gradient descent, but missed some extras like lemmatization and HMMs in NLP...

by Anatoly D


C​ompared to other courses (esp. Adrew Ngs) very low in-depth explanations and challenge level.

by Anderson G S


Although the course presents an overview of the topic, I was expecting a more advanced and deeper approach.

by Harsh G


Didn't Feel Like I am learning some concept very basic concepts nothing related to real life and NLP

by Susie B


In general, good. M​isspellings in assignments is not very professional, should be revised.

by Phillip


Would be good if there are more checkpoints to see if the codes are correct or not.

by Kestin C


Some example is hard to understand, and few of the diagram is ambiguous.

by Alex A


Especially later excercises contain code/instructions that are unclear

by Luiz O V B O


I would like to have more content and explanation about the math

by john s


I don't feel the assignments help understand the material.

by Huang J


The videos are too short. Discussions are oversimplified.