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

4.7
stars
1,201 ratings
210 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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26 - 50 of 210 Reviews for Natural Language Processing with Probabilistic Models

By Simon P

Nov 27, 2020

Simply, it's not great.

The assignments are long and complex, with insufficient checks to debug when there's an error. The theory is poorly explained in both the videos and the labs. They clearly do not know who this course is aimed at; is it software engineers who want to better understand NLP? In which case they may find the assignments easy but the content lacking. Is it people with a basic understanding of NLP but want to take it further? In which case they will not get that, given that the concepts are only briefly discussed. Is it as a general introduction to NLP? In which case the coding aspect is at too high a level, you have to be familiar with all the little python tricks they know and to think in the same way they do. This leads to a frustrating experience.

By hovering the cursor over the names of contributors in the discussion forums, it is clear that most of the people who start this course never finish it. This level of attrition reflects poorly on the course creators.

By Dimitry I

Apr 14, 2021

Very superficial course, just like the rest in the specialization. Quizzes and assignments are a joke. Didn't want to give negative feedback at first, but now that I am doing course #4 in the specialization, which covers material I don't know much about (Attention), I've realized how bad these courses are. Very sad.

By Dave J

Jan 25, 2021

This course gradually ramps up the sophistication and interest from the first course in the NLP specialization.

Week 1: Autocorrect and Minimum Edit Distance is OK, nothing to write home about but gives you a sense of how a basic autocorrect mechanism works and introduces dynamic programming.

Week 2: Part of Speech Tagging explains Hidden Markov Models and the Viterbi algorithm pretty well. More of a sense here of learning something that will be a useful foundation.

Week 3: Autocomplete and Language Models explains what a language model is and builds a basic N-gram language model for autocompleting a sentence. Again, good foundations.

Week 4: Word embeddings with neural networks was for me the most interesting part of the specialization so far. The amount of lecture & lab content is considerably higher than in the previous weeks (which is a good thing in my view). The pros and cons of different ways of representing words as vectors are discussed, then different ways of generating word embeddings, from research papers dating from 2013 to 2018. The rest of the week focuses on implementing the continuous bag-of-words (CBOW) model for learning word embeddings with a shallow neural network. The whole process, from data preparation to building & training the network and extracting the embeddings, is explained & implemented in Python with NumPy, which is quite satisfying.

I found that the labs and assignments worked flawlessly. They are largely paint-by-numbers though, I would have liked to have been challenged and made to think more. The teaching is pretty good, though there's room for improvement. It tends to focus a little narrowly on the specific topic being covered and has the feel of reading a script. What I would like to see is more stepping back, thinking about and explaining the larger context of how the topic fits into current NLP and the student's learning journey; then engaging with the learner on this basis. I did feel this course was a little better than course 1 in that regard. Overall 4.5 stars but as there are no half stars, I'm going to let week 4 tip it up to 5.

By Yuri C

Dec 29, 2020

I enjoyed very much this second course in the NPL specialization! I must say, once again the balance between mathematical formalism and hands-on coding is just on point! This is also not easy to achieve. I quite enjoyed also the infographics about the word embedding model developed during the course. I have been reading blog posts and papers about the technique for some time now and I did not see any best explanation than the one in this course, chapeau! Nevertheless, there are also points of improvement to consider. One of my main concerns is that at the end of some assignments, there is very little discussion about the validity and usefulness of what we get at the end. Although in the motivation a lot is being put forward. For example, while building the autocomplete, there were a lot of time dedicated to motivating why is this useful and why one should learn, but at the very end of the week, when we finally build one with tweeter data, there is very little analysis on these results. This is a bit frustrating. Of course, one cannot build very useful models while in an assignment in a Jupyter notebook, nevertheless I am positive that you can find also here a good balance between analyzing the model's outputs and inquiring if indeed we achieved the goal we set at the beginning, and if no, why not, etc. Clearly, assignments are not research papers, but a bit more careful treatment on that end will make this course achieve its full potential. Keep up the good work!

By Leena P

Oct 5, 2020

I enjoyed Younes's teaching style and the specializations course structure of asking the quizzes in between the lectures. Also the ungraded programming notebooks give grounding and hints while allowing the graded work to be challenging and not completely obvious. Thanks to all the coursera team for sharing such deep knowledge so universally and easily. This knowledge sharing to all that seek it, is what I think is the hope for AI to stay relevant and not get lost in hype.

By SHASHI S M

Dec 25, 2020

I learned auto-correction using minimum edit distance algorithm, part of speech tagging by Viterbi algorithm, autocomplete using n-gram model, word embedding by applying Continuous Bag of words models. This week was a little tough and got great hands-on experience in NLP. Change my thought about NLP. This week was amazing. I work on nltk library, created a neural network to train a model for word embedding.

By Nishant M K

Mar 31, 2021

Great course! As course 1 in this specialization, the REAL value lies in the Python/Jupyter notebooks that have a great mix of filling out key steps, along with very detailed and pointed descriptions. The lecture material is also very helpful in 'orienting' students and the coding assignments are where the actual learning happens. I would very much recommend this course!

By Nilesh G

Jul 29, 2020

Greta Course, Nice Contents from basic to advance NLP...coverage of topics about word embedding,POS, Auto Completion was very good, assignments are challenging one but learn lot of things by hand son practice, hints are useful ..looking forward to complete remaining courses from this NLP specialization...Thanks to all instructors

By Cristopher F

May 8, 2021

This is an exciting course. This course will not make you 100% ready for the real world, but it will give you directions that you can follow by yourself. I think the purpose of learning is not to be stuck somewhere while losing your mind. It's to build a foundation where you can find your own path.

By Yixuan Z

Jan 8, 2021

Most knowledge is new to me, but I really enjoy all the course content. I hope the autocomplete model could also instruct me how to predict new 2-5 or even more words based on N-gram models. The assignment of autocomplete only includes cases that predict the next 1 word.

By Soopramanien V

Sep 30, 2020

Great course to learn word embeddings, the instructors are excellent at explaining key concepts in a very clear and concise way and the accompanying assignments and labs serve their purpose in getting hands-on experience with implementing many of these NLP models

By Prantik B

Aug 29, 2020

The overall contents are very much interesting & also helpful. But week2 & 3 was a little bit harder for me. So I think, those contents can be little bit more informative so that anyone can go through the week's assignment more clearly.

By Usama I P

May 16, 2021

Best Course for studying NLP. I started NLP as an experiment but these guys made me fell in love with NLP with such a clear and in depth explanation of everything that I feel so confident. Thank you for such an awesome course.

By Cecilia E G R P

Sep 8, 2020

Excellent course, the explanations given by Professor Younes were very clear. It allowed me to learn more about how natural language processing is done on the inside. Thank you to all the teachers for sharing their knowledge!

By Dustin Z

Aug 22, 2020

A good course that covers several important probabilistic models in NLP. Very good balance between challenging and easy. There are also some interesting software concepts like dynamic programming discussed. A fun course!

By A V A

Oct 25, 2020

Excellent and detailed description of how autocorrect and autocomplete work, as well as how POS are tagged based on Markov Models and how word embeddings are derived using a CBOW model.. thoroughly enjoyed this course!

By Christoph H

Jul 1, 2020

This course goes hand in hand with the Daniel Jurafsky's introduction to NLP (Speech and Language Processing) and provides the knowhow for hands on implementation of simple but powerful probabilistic methods.

By vishal b

May 3, 2021

Amazing course , just loved the way faculties has explained the complex concepts in such a easy manner and also hands-on labs and graded assignment are very helpful to review one's understanding of concepts

By Noah M

Dec 13, 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).

By Harshavardhan S

Dec 3, 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!!

By Sazzadur R

Aug 4, 2021

Another great course introducing the probabilistic modelling concepts and slowly getting to the direction of computing neural networks. One must learn in detail how embedding works.

By Aniruddha S H

Sep 29, 2020

Very good course! helped me clearly learn about Autocorrect, edit distance, Markov chains, n grams, perplexity, backoff, interpolation, word embeddings, CBOW. This was very helpful!

By Bharathi k N

Sep 11, 2020

This is one of the best courses i have taken. I have learned a lot from this course. Assignments were great and challenging. Thank you deeplearning.ai team for this amazing course.

By Aditya h

Oct 9, 2020

Thoroughly relished this course. Each and every concept is explained in depth as well as there is a companion notebook to explain as well as practically implement the concepts.

By Kazuomi K

Jul 1, 2020

This course is very good introduction to NLP Probabilistic models such as Hidden Markov model, N-Gram Language model, and Word2Vec with Python programming assignments.