Chevron Left
Back to Natural Language Processing with Probabilistic Models

Learner Reviews & Feedback for Natural Language Processing with Probabilistic Models by DeepLearning.AI

4.7
stars
1,654 ratings

About the Course

In Course 2 of the Natural Language Processing Specialization, you will: a) Create a simple auto-correct algorithm using minimum edit distance and dynamic programming, b) Apply the Viterbi Algorithm for part-of-speech (POS) tagging, which is vital for computational linguistics, c) Write a better auto-complete algorithm using an N-gram language model, and d) Write your own Word2Vec model that uses a neural network to compute word embeddings using a continuous bag-of-words model. 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

NM

Dec 12, 2020

A truly great course, focuses on the details you need, at a good pace, building up the foundations needed before relying more heavily on libraries an abstractions (which I assume will follow).

HS

Dec 2, 2020

A neatly organized course introducing the students to basics of Processing text data, learning word embedding and most importantly on how to interpret the word embedding. Great Job!!

Filter by:

251 - 275 of 286 Reviews for Natural Language Processing with Probabilistic Models

By Gopal M

Sep 5, 2020

Assignments were incorrect.

Lot of content was squeezed in the last week. Even spread would be ideal

By Aung Z P

Jul 14, 2020

I love the way the instructor teach and the course design which is made to be simple but effective

By Aman M

Sep 14, 2023

In week 4, the formula for the partial derivative of the cost function wrt b2 is not mentioned

By John F

Feb 19, 2022

great material and presentation, there are a few typos here and there but not a big deal

By Mounir H

Dec 1, 2020

I didn't like weeks 1 and 2 too much but I liked week 3 and I really liked week 4.

By bdug

Apr 5, 2021

I liked the lecture, very well prepared. Only the part on metrics was a bit short

By Vladimir V

Jul 20, 2020

This is a good course but I would like to see more emphasis on the mathematics.

By Manuel V B

Apr 11, 2021

Great course, but the last week felt a bit messy with submission evaluation.

By Pedro P

Feb 12, 2024

Algebra could be explained more in depth, but all in all, good course

By Sophie Z

Jan 3, 2021

Not sure if it is on purpose, but W4 labs have repeating content.

By Vi T T

Dec 4, 2021

wonderful course, I learned the fundamental of NLP. Many thanks

By John

Sep 1, 2022

Good content and presented in an easy-to-learn fashion.

By Muhammad A

Jan 13, 2024

Could be improved with better visualizations.

By Jinsong T

Jun 8, 2021

Too basic and going at too slow a pace

By Esakki p E m

Apr 11, 2021

excellent Material & teaching

By Roshan k

Aug 6, 2023

week 2 need bit more help

By AVIJIT J

Aug 16, 2021

Good, very good.

By Randall K

Apr 14, 2021

great course

By ugur b

Jan 2, 2022

Veri good

By MoChuxian

Oct 31, 2020

great

By Deleted A

Sep 5, 2021

good:

* some of the content is well-explained

* provides good solid knowledge about the background and implementation of common NLP tasks

less good:

* notebooks (and content generally) are unevenly distributed

* significantly stronger focus on ML, rather than on the NL side (this is consistent throughout the specialization)

* some of the explanations (e.g. in week 2) aren't clear

* specialization could be structured better -- word embeddings are introduced in course 1, but the in-depth discussion is here in week 4; would perhaps have made more sense to have that content build on itself

By J N B P

Mar 5, 2021

In this course, you will learn to build an autocorrect model and different methods of building this model. The course felt a bit rushed with a lack of detailed explanation, students who are familiar with the concepts of NLP from before starting this specialization won't face any problem, but students who had just begun learning NLP through this specialization might feel a little difficult.

By Gent S

Apr 8, 2021

The course material is good and you can learn new things, you can exercise python skills a lot as the assignments are quite long. However, the tutors are not the best in explaining the material as well as the videos are a bit vague. It would have helped if the tutors were a bit more experienced in teaching, but still overall good!

By Aditya J

Aug 14, 2020

well I did deep learning specialization earlier things are mathematical, but here they don't go much into maths, and please make some concept chart, to link different algorithms.

By Chi Z

Jan 5, 2021

BIg bug in week4's assignment! I don't know why not fix it. It turns out that I just train a dummy network