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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....




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



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.


Natural Language Processing with Classification and Vector Spaces: 651 - 675 / 704 レビュー

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.

by Renato G


It is an interesting course to learn the basics of NLP

by Anish S


good for beginners, but needs more advanced concepts.

by Sonam G


The explanations in the videos could be improved.

by Deleted A


No longer required. Beyond my present knowledge.

by Shayan J


Content is verbose and locks context in places

by Lorena P


I believe that explanations where too shallow

by Zaid A


v​ery good course, a lot of stat and math

by Sihao L


So many small mistakes here and there

by Harshita B


I didn't quite get the feel of it

by Spandan.Pandey B


Problems in week 3 Assignment

by jkf


Just ignore the video!

by Rishik R


Too easy

by Dmitriy I


Too easy

by Christoph H


I believe the course does not allow you to study NLP in depth. Compared to the deep learning specialisation by, 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


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


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


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.