The main reason for my rating is because the course is so "loose" on what your are supposed to achieve incrementally every week that it can lead to some hard situations.

Just to give my example: the first week was piece of cake and I didn't feel like it really contribute for the following weeks. Then, I was struggling with the suggested library (tm) until I got support through the discussion forums and someone suggested me to use quanteda.

Then thinks started to run smoothly, or so I thought. When implementing the language model (which, at first, I thought was supposed to be KBO), I got stuck for a long period. Not because my model was wrong (I was able to implement it and to check it against some hand-written and proved examples - which I should probably thank again), but because I was not able to make it run efficiently enough for the given constraints.

Being stuck in this stage for longer than I wanted, I had to sacrifice another important steps of data analysis pipeline in order to not jeopardize my final delivery by not meeting the final due date. I know that this is exactly what will happen in the "real" life, but I think that some better guidance could guarantee the students spent a more even amount of time in across all steps.

All things considered, I think the Capstone was really interesting and likely took more than the 4-9 hours per week, but most of this is probably because of the problems I faced.

I believe that with a better guidance on the paths to follow or maybe some suggested libraries to use, a lot of "noise" (useless difficulty) could be removed and this course would definitely get more starts.

Could be better. The teacher sometimes explain the concepts in a hard way, and not always shows how to do in practice.

But you will get curious and in case of doubts, you can find more simple explanations on the web, and the forum is very good.

The assignments are hard, you will need do research to accomplish then, but is the best way to learn.

I think the specialization is good to someone without much knowledge on the field (like me). But it's only the start!

I felt this course was the weakest of the series. The capstone focuses on building an NLP application, which although I find interesting, does not make for a good final problem as NLP was not really covered in the specialization and NLP is particularly challenging in R. That said, the series as a whole is well worth the time and effort.

It's been a challenge to learn all these new concepts and package them into a working product in such a small period of time. I am glad of the things I learned. Also, in my opinion the materials / resources given to this course are scarce compared with previous courses of the specialization.

Hi, the prject was nice and at the end I learned some new things, but it didn't have people to provide any guide. In the videos it was said that personal from SwiftKey will be there as well as JHU teachers could provide some insights. It looked a bit like a phantom course

It's an inspiring project in the field of NLP, however, the major concern is that this topic and the corresponding skills have never been introduced before the capstone project.

I would say, if we could introduce a capstone project more related to the first

NLP module should definitely be included into JHU Data Science specialization.

Had to learn a lot on our own but very valuable content once acquired.

Most stressful but interesting. Not enough material was provided

I liked every course in this specialization except

it could've given more instructions!

With the exception of R Shiny programming, there was nothing about this course that required any real knowledge of anything in any course of the JHU Data Science certificate track. Why do you ask? Well, most of the class was just about learning natural language processing (NLP), which wasn't covered. What about R programming, you ask? Most of the NLP packages in R that I tested out couldn't process a 200MB text file in a reasonable amount of time or with a reasonable memory footprint. I ran Python and R programs in parallel to do sentence and word tokenization, and Python's nltk was (not exaggerating) 100x faster than R's NLP package, and R's tm package took 4GB of memory to parse the same 200MB corpus. In 2018, that's just unacceptable. There's no way you could ever write production-quality NLP code using these R packages. After the course was finished, someone pointed out an R package that could adequately accomplish the task, but by then it was far too late. Even R's basic data structures themselves weren't up to the challenge. I ended up building my model in Python, exporting it as JSON, and then importing that into my Shiny app. Comparing basic data structures in Python and R to represent the same JSON file (i.e., just read in the file and measure the size of the resulting object), R's list was nearly 2x as large in RAM than Python's dict. All of this combined with really very little reference to most of the material in the other nine classes in this track left me very disappointed. The reason I gave the class two stars and not one was because what we did learn about NLP was useful. Having to solve a gnarly, real-world problem starting from raw data is useful. Having to write an app with actual users interacting with it is useful. But could just about everything about this class have been done a lot better? Yes. I think a machine learning project that tied together everything that we'd worked on up until this point would have been a lot more fun and rewarding.

Of all the offerings in the specialization, this one felt like it was thrown together in less than hour. I expected to have to learn quite a bit of material on my own, but even the references to additional materials were very thin.

I could have saved many days if more guidance on the project workflow would have been given. The pre-processing of the data was quite extensive (9 steps before generating the ngram tables I used in my model) and was the key to getting decent results IMHO, but one had to step on a quite a few landmines to figure this out.

The problem was an interesting one and I ended up reworking it after passing with 95% (the only class in the specialization I didn't get 100% on) because I didn't have time to implement much of what I had to figure out by 'hard-knocks'

Unfortunately this project is not fully aligned with all the previous program, which is a shame. Ideally, the project was more related to quantitative data, or have compulsory module NPL. It was certainly a very important learning, but very stressful to have to grasp NPL and do the project in a short time.

Learning NPL in short time in a DIY way without any help it was very negative and stressful.

As other reviewers said, the Capstone is too unconnected to the rest of the specialization. In the end, there is no metric as to what makes your model successful, it's just the Slides and the appearance of the Shiny app that counts towards the total mark. Also, the topic (Natural Language Processing) is just too unconnected to anything seen in the other courses. It was fun, but felt a bit off.

The series leading up to the capstone was excellent but the capstone itself was a disappointment. Very little instruction was provided and the grading criteria were flawed. Also, most of what we learned in the first 9 courses about statistics and machine learning turned out to be irrelevant to the capstone project.

The course has nearly nothing to do with the previous themes. I already have had enough knowledge, but as there is no support by the team it seems to be rather time consuming for others.

I hope it will involve statistics analysis in the capstone project. It is kind of bias to apply NLP knowledge and develop data product in the capstone project.

Not enough information on how to approach the final project. The project itself is very time consuming with lots of self learning and unclear specifications.

The capstone project doesn't fully utilise d knowledge from earlier modules such as Machine Learning, statistical analysis, regression models n etc.

No physical way to complete the class within one session. Little is learned, no instruction is given, just build a thing that sort of works.

The course is outdated and abandoned by the teachers.

SwiftKey engineers are nowhere to be seen.

There is no guidance.

Thanks for the guide but I did the hole course without instructions, there were new thing that could be tougth.

need more details