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

4.7
stars
1,200 ratings
209 reviews

About the Course

In Course 2 of the Natural Language Processing Specialization, offered by deeplearning.ai, 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 important 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. Please make sure that you’re comfortable programming in Python and have a basic knowledge of machine learning, matrix multiplications, and conditional probability. 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!!

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176 - 200 of 210 Reviews for Natural Language Processing with Probabilistic Models

By James P

Sep 17, 2020

I found the course really helped to reinforce my understanding about importants concepts like n-grams, HMMs and word embeddings. The labs are pretty well spread out, and by the time you get to the week-ending assignments, you have all the info you need to complete.

By Will H

Mar 31, 2021

The lectures on the Viterbi algorithm were a little wooden and there were no summary text (reading) tasks (as there often is in other deeplearning.ai courses), however this is a worthwile and informative course.

By Rafael C F d A

Jan 16, 2021

In the first and second week the exercices have some unecessery pranks in the data formatation just to make the exercice harded, but it take out the attention for what matter in the course that is NLP

By German C M

Dec 30, 2020

Very good to see how the "from scratch" concepts are presented; nevertheless, I have the feeling that a very little "real use case" problem has been presented, with tiny sentences being analyzed.

By Osama A O

Oct 7, 2020

Good course, but the lecture notes in week 2 can be much more improved. Understanding Viterbi algorithm without visuals and animations was very difficult. Apart from that, great course!

By Ramprakash V

Aug 19, 2020

The course is exceptional in its own way by bringing people to the understanding of probabilistic models. Crisp & Clear. But one need to explore & practise more to gain expertise.

By Cheng J

Sep 9, 2020

The Viterbi algorithm introduction is a bit hard for us to follow. Probably some writings may help guiding through each steps.

By Hernan J

Nov 4, 2020

Esta especialización junto con la de Deep Learning se complementan y es son más claros los conceptos y prácticas, gracias!

By Zoutao W

Mar 25, 2021

The tutor sometimes pass the slices too swiftly. I hope that he could wait 2-3 seconds after finishing speaking.

By Sandeep V

Oct 2, 2020

Sone Quiz should also be there. Assignments can be solved by python knowledge an following the instruction

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 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 Sophie Z

Jan 3, 2021

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

By Jinsong T

Jun 8, 2021

T​oo basic and going at too slow a pace

By Esakki p E m

Apr 11, 2021

excellent Material & teaching

By AVIJIT J

Aug 16, 2021

G​ood, very good.

By Randall K

Apr 14, 2021

great course

By MoChuxian

Oct 31, 2020

great

By Teresa M B

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 Amlan C

Sep 17, 2020

Too many gaps in the course. Many concepts not covered in the mathematical sense basic Grad. Desc. math would have been helpful. Also if you want to omit it totally you should have atleast one lab on how one would do it in real life using which library? Pytorch? Keras? What? Rest of the course is okay. Younous is great in explanation.