Very good course from deeplearning.ai team , you will need some background in ML and Python. The support on the coursera forums could be better.

Academically, a step down from deeplearning.ai's previous courses. In terms of technical quality, the media could use improvement, particularly in normalizing audio levels and ensuring a perfect acoustic setup for all lecturers. Coursera should hire some acoustics and motion pictures experts and task them with improving "production values" (look it up).

Shortcomings notwithstanding, this is still a great class and a "must take" for any aspiring NLP expert.

This course has plenty of wonderful material and very deep content, however the assignments and quizzes seemed to be fairly trivial in testing knowledge. I appreciate the material and will keep it as references for NLP projects in the months ahead, but I do not think that passing the coursework is indicative of learning the concepts in any meaningful way.

HW check is just awful. I need to use exact these functions (i.e. squeeze instead of ravel).

Moreover, one could complete homeworks without referring to the lectures at all -- zero challenges met.

The last HW is not balanced (11 tasks -- too much compared to the rest).

I would suggest the course makers to redesign the homeworks dramatically.

Thank you first of all. I think the length of videos could be longer for better understanding of concepts. Specifically, I think in Week 4 there are a lot of concepts like ( Locality Sensitive Hashing) that could be better explained.

The assignments lacked clarity. The huge number of variables used for seemingly trivial intermediatory task is really confusing. Also, the assignment documentation and instructions need to be enhanced,

The exercises and assignments seemed to place more emphasis on the coding and less on the theory and study of natural language processing per se, imho.

I think the content of the videos and slides is quite insufficient to understand the topics

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a shallow, the lessons doesn't go into details. The teacher only shows what's needed for understanding the week exercises. I won't dare say it's even basic level.

Really too elementary. You can skip almost the whole course if you attended the DeepLearning specialisation.

By far the worst course I have ever done.

The 'lectures' are just a few minutes long and as expected are very thin. Moreover, the majority (yes > 50%) of the lectures are spent explaining basic linear algebra (much of which is high school level) and hardly any time is spent on NLP itself. Most of the core NLP concepts are barely mentioned and it is obvious that the labs were written by someone else than the person who wrote the lectures as terminology gaps / differences exist and as the content is very thin it's impossible to put two and two together.

The readings are simply annotation summaries of the already brief lectures. They add no value.

The labs do not support learning in anyway. They are simply a 'fill in the gaps' exercise where > 50% of this would simply be figuring out which variable you need to index inside a loop. It is a joke.

The only good aspect of this course is the slack community and is the only thing I can recommend. If you can sign up for the trial for the course / specialization and participate in the slack community. Apart from that, the remainder of this course is a huge waste of time: you won't learn NLP here, you will simply have a guided python tour with NLP problems as content.

I came to this course motivated and excited. However, after having taken the machine learning specialization taught by Professor Andrew Ng, I was disappointed by the quality of this course. Not only does this course go too fast and doesn't explain the intuition and reason behind concepts, the labs are outdated and have terrible unit tests. I work by day and study by night, only to find a programming assignment (week 2) that has terrible unit tests. It tells you that 12 tests passed and 3 didn't. Why? Who the hell knows why? Which defeats the concept of a unit test. Then I found myself debugging the unit tests themselves to understand why the they fail. I mean, calculating the loglikelihood is straightforward, and yet, it said that the tests failed. Go figure! I do NOT recommend this course!

This course did not give me any practical skills, and the theory was also superficial. Programming blocks in which 95% of the code is written for you and you only need to substitute a few functions and variables do not make sense. I can't remember the last time I had so little motivation to finish the course, and I don't plan to take the next blocks of this course.

I am waiting for my certificate for over a month now, there seems to be the problem with the grader that wont mark my assignment as passed. I reached for support multiple times now and no matter how easy the problem is or how important to me it is, nothing is done. Pretty dissapointed. In general the course is great.

Very disappointing.

The course aims at teaching some copy/paste recipes for algorithm rather than an understanding of the theoretical basis behind these algorithms. Plus, the assignments are about dealing with numpy/python syntax, there is no thinking about how to implement an algorithm as everything is pre-entered.

there is no note provided

the programming questions and instructions are so ambiguous

this led to much confusion

I keep getting unexpected error at lab 4 and cant not finish the course

I recently completed the course on Natural Language Processing with Classification and Vector Spaces, and I found it to be highly informative and well-structured. The course provides a comprehensive introduction to key NLP concepts, including vector spaces and classification techniques. The content is presented in a clear and organized manner, making complex topics accessible even for those who are relatively new to the field. The course includes practical examples and exercises that are useful for reinforcing the theoretical concepts discussed. I particularly appreciated the focus on hands-on applications and real-world scenarios, which helped bridge the gap between theory and practice. One area for improvement would be the inclusion of more advanced topics or additional case studies to challenge learners and deepen their understanding. Additionally, some sections could benefit from more detailed explanations or supplementary resources to further clarify complex ideas. Overall, this course is a valuable resource for anyone looking to build a solid foundation in NLP and vector space models. I would definitely recommend it to both beginners and those with some prior knowledge who wish to refresh and enhance their skills

I enjoyed the course very much! I would say, there is still some things to improve, like the treatment of the implementation of the hash-local search, or some typos here and there along the way in the notebooks. Another suggestion is to postpone the implementation of SGD until after you get some warm-up in numpy during week 2-3. All in all I quite enjoyed the approach to be able to build the tools myself with numpy. This way, it demystifies the use of pre-built packages and lets the learner indeed understand what is going on under the hood. Of course, there is always a trade-off between being precise and being easy to understand. I think nevertheless that this course is spon-on in this trade-off optimization task. ;)

This is an amazing course for beginners in the field of natural language processing. It starts with the very basics of machine learning and natural language processing. Exploring sentiment analysis using two different approaches one is a frequency-based approach (involving logistic regression) and the other is a probabilistic approach (involving naive Bayes approach). This course also gives a glance of vector spaces and techniques to convert multidimensional data to lesser dimension data using approaches like PCA. I learnt new topics like locality sensitive hashing during the last week of the course. It was a fun and engaging course overall :)

I think this course really opens up the student to the field of NLP, the concepts shared are simple enough to understand, and the codes shared in the lectures / tutorial labs are really useful to understand how the theory is translated into python codes and applied into working models. I love learning NLP! When I am learning NLP I am Happy indeed with DeepLearning.AI, thank you Professor Andrew Ng and et. al. The Training Instructors do explain the concepts and application codes very well. My heartfelt thanks and gratitude to this course!

I really like the way the course is structured and the way it is taught. The language is clear and it goes at a good pace. I have completed the Deep Learning Specialization as well and felt that this course had a better segregation of material/videos. The short videos make sure you don't lose focus or get bored. Content-wise, the course is great for understanding the initial steps required for approaching NLP problems. Highly recommend it and I look forward to completing rest of the courses in this specialization.

No matter who you are or who you have been, you can be whoever you want thanks to this course you will understand in great detail the Natural Language Processing with Classification and Vector Spaces. The teacher rigorously explains its content by putting you in a real situation and the key concepts are very well explained in clear the academic rigor is there now I can affirm without reserve that I understand very well several concepts which were strange for me thank you to coursera and deeplearning.ai

This course had helped me become familiar in natural language processing. Before taking the course, I feel that NLP is already like a plug-and-play thing due to deep learning. However, it had helped me understand the importance of preprocessing especially to get the right embedding. Additionally, it also help me understand a glimpse of doing sentiment analysis and Machine translation. Indeed, I still have many things to learn but this course is a great way to be introduced on the NLP.

Great intro to the world of NLP. For those with a background in python, linear algebra and statistics (my case) course will take significantly less time, as you can skip some of the videos and rush through easier notebooks. Assignments will make sure you haven't overlooked parts you don't yet understand. In my opinion comments and hints sometimes lead by the hand a little bit too much, but maybe I'll change my mind when faced with assignments from next courses of the specialisation.