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

4.8
stars
747 ratings
128 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

AH
Sep 28, 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!

SK
Jul 13, 2020

I have a wonderful experience. Try not to look at the hints, resolve yourself, it is excellent course for getting the in depth knowledge of how the black boxes work. Happy learning.

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1 - 25 of 130 Reviews for Natural Language Processing with Probabilistic Models

By Oleh S

Aug 3, 2020

This course is more mature than the first one. Materials are very interesting and provide nice intuition for the probabilistic models. One can study the basics of auto-correction, Markov and Hidden Markov Models as well as the N-gram models and very important approach - Word2Vec, which is the essential part of the modern deep learning algorithms. I really enjoyed this part.

However, there are some minor suggestions:

1. Lectures duration could be longer - it will help to provide more depth in materials in both math and code side. I know that this is a simple version of real academic course, but in order to increase the quality you should consider the increasing duration;

2. Programming assignments are not balanced and there are still some minor ambiguities. For instance, the first and HMM assignments are tough, whereas the last one is a piece of cake.

3. The course can be enhanced with the additional part dedicated to Probability Theory, maybe a few lectures more.

I recommend this course to everyone interested in NLP. Note, you should read and study the additional resources to reinforce your knowledge, here is just the starting point for a good researcher. Keep going, guys!

By ES

Jul 7, 2020

Homework is too easy. The answers are pretty much given to us.

By sukanya n

Jul 21, 2020

Viterbi algorithm could be explained better and Week 4 seemed very rushed with lots of details just glossed over. The assignment of week 4 compared to previous weeks seemed pretty easy.

By Zhendong W

Jul 10, 2020

A great course indeed! However, it would be even nicer to have the lecture videos in a slower pace, maybe go through the examples in more detail. Sometimes it felt too quick to jump directly through the theory to examples.

By Mark M

Jul 19, 2020

This second course like the first feels like a first or second year university course. Sometimes the explanations are weak or missing. There was no explanation for why the Viterbi algorithm works, no explanation for how to decide which embedding extraction method (W1 columns, W2 rows, or average of the two) method to use. There seemed to be little or no TA support. Many people were posting questions and not receiving answers from TAs. I posted the mistakes I identified in the course content, but I don't think anyone is going to act on this. It would have been good if the last exercise were repeated in Tensorflow. Also it would have been good to actually use the embeddings for something in the last exercise. From the PCA graph, the embeddings looked pretty poor.

By Dan C

Jul 8, 2020

Lots of Quality Control issues. using paid customers as proofreaders is tacky.

By François D

Jul 17, 2020

Great teacher, good pace in lectures and assignments. There are of course some redundancies wrt the previous specializations but it's nice to feel that you understand the content a bit better every time. Didn't find the forums (internal & slack) very useful, could be better structured. Can't wait for the next 2 courses.

By Manzoor A

Aug 20, 2020

Excellent! I know this course is the beginning of my NLP journey, but I can't expect more than this . The ungraded labs are very useful to practice and then apply it to the assignment. I am giving 5 star because There is only 5.

By Sohail Z

Aug 19, 2020

Brilliant course!!!! love it every aspect of the course. i am really grateful to the deeplearning.ai team for such amazing courses. they are easy to digest and provide sufficient math knowledge to understand the models.

By Alan K F G

Aug 21, 2020

Professor Younes really makes easier for me to go along the lectures and to be focus. The structure of the course helped me a lot to constantly review the same concepts as I went further in order to learn new things.

By Saurabh K

Jul 14, 2020

I have a wonderful experience. Try not to look at the hints, resolve yourself, it is excellent course for getting the in depth knowledge of how the black boxes work. Happy learning.

By Kritika M

Aug 10, 2020

This course is great. Actually the NLP specialization so far has been really good. The lectures are short and interesting and you get a good grasp on the concepts.

By Andrei N

Jul 11, 2020

A great course in the very spirit of the original Andrew Ng's ML course with lots of details and explanations of fundamental approaches and techniques.

By Minh T H L

Jul 31, 2020

Thanks for sharing your knowledge. I am happy during the course and I also leave a couple of feedback for minor improvement. All the best.

By Ajay D

Aug 17, 2020

Course was very insightful about the latest enhancements in the field of NLP. The exercises designed was very hands on and I loved that. However I felt a bit incomplete as I didn't see any large dataset in action, maybe my expectation was wrong. I was also wondering if I can get to see some more applications of these language model and word embeddings in the course.

By Kravchenko D

Aug 21, 2020

Nice course, but assignments in this course are less practical than in the first course of this specialization. The last assignment in this course was implementing the word embeddings generation using your own neural network. The whole process of writing your own neural network is nice except the resulting word embeddings that look very bad and ungrouped on the plot and the text in the notebook says: "You can see that woman and queen are next to each other. However, we have to be careful with the interpretation of this projected word vectors" without an explanation of what's wrong with the results. So I think that the last assignment should be reworked by the reviewers to have illustrative results at the end, not just "Word embeddings at the end are bad. Bye-Bye, see you in the next course"

By Manik S

Aug 13, 2020

Although the content is great but the way of teaching is lacking relative to how Andrew teaches in Deep Learning specialization. More interactive teaching with a pen tablet would be more engaging. The whole course seems like a recital of the slides. And the intructor's voice is a little bit irritating to listen to over longer durations. Otherwise the course provides a lot of learning if you can bear it.

By Gabriel T P C

Aug 3, 2020

To lessons are shallow, exercises to repetitive.

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 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 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 Cecilia 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!