Interesting case study.

Indispensable for those who want to master the R language along with data science. I highly recommend this 10 courses. Believe me you will not regret it.

The assignment was designed very well. I struggled and was thinking of giving up. I'm glad I didn't. The assignment actually required all skills I learnt previously. A bit time consuming but achievable. Thank you very much!

It was a challegne for me, but also a fun!

Very interesting experience in a new promising field!

Like diving in without learning to swim first - but man did I learn a lot.

Great Course

I took this specialization a couple of months ago and did not comment as such. Now I turned around to remember some topics and started reading comments.

I found many comments that say the final project has nothing to do with the previous 9 courses and when I did it I thought the same.

Looking at it in perspective, I think the previous courses are absolutely necessary for the final project. The objective of carrying out a project with such characteristics is to apply the knowledge by oneself.

The first courses of programming in R, extraction and cleaning, and exploratory analysis are fundamental to understand the problem. In this case the cleaning has to do with the transformations using regular expressions and tokenization. The exploratory analysis should be done in any data science project, otherwise you may encounter surprises when implementing the models.

Statistical inference was necessary and closely linked to exploratory analysis, especially to select samples well and review distributions, since some machine learning methods may be affected by distributions. I must say that I did not see this when I took this course, but it was because of my lack of experience. Maybe there was a lack of guidance.

The algorithm I used was regression on the ngrams for simplicity, time and capacity of my computer, but it could have been combined with other methods such as neural networks or svm.

Implementing the model in shiny and then adjusting it because it was very heavy was also interesting.

As a summary, I really liked this specialization and although it was very hard and many times I did not know how to move forward (especially in the capstone), I think the challenge was important for my learning and I was very entertained.

Thank you very much for this course

helpful

Hard, extremely satisfying.

Great Course, great experience

Very nice course and there was a lot to learn. Awesome!!!

Thanks Professor

Challenging. The course could possibly offer a 'here's how it could be done' ideal example after final submission and pass.

I appreciate all the work they put into creating the course,. However, it can be frustrating to follow. It would be nice if they would structure it in a more organized fashion.

I wish that either there were a choice of capstone projects, or that there were a more numerical component to the analysis than such a pure text based assignment.

Great project.

The course project was very interesting. It can be challenging if you want to do it properly or easy if you just want to pass. I tried to do it properly for which I had to repeat the course 3 times, but in the end it was good - I think I learned a lot.

Good project. Got me to think outside the box and really challenge myself.

I enjoyed the course. This course took me waaaay more time than I thought because I struggled with a few issues. First, I wish I'd started by taking the NLP online course before starting the Capstone (https://www.youtube.com/watch?v=-aMYz1tMfPg). There was an issue installing RWeka, RJava and it took me several days to work through the issues. I eventually moved to using quanteda (https://cran.r-project.org/web/packages/quanteda/vignettes/quickstart.html). I also waited far too long to develop a method to test my model using a subset of the training data, so I could test whether changes to my model improved and reduced performance. It turns out that my model trained on a 25% sample performed just as well as a model trained on 100%. I'm thankful for the Discussion Forum and final peer review process. Both helped me learn how I can improve my model and demo application. I really appreciate the instructors for creating this specialization. I've learned a lot.

Very interesting, but Capstone is much more difficult than the rest of the course.

Be sure to study carefully the first courses, and don't rush.

A tricky end to the specialisation - but quite a lot of fun.

It was a challenging project and really pushes you to learn and manage on your own. It also pushes you to build and end to end product within time and memory constraints. Learned a lot during this project!!

Thanks!!

Great course, great content, didn't like the final capstone project though.

A very tough and challenging project, but a great way to learn a lot about Natural Language Processing and algorithm coding in R, and in the end to have a cool Shiny app to add to your portfolio. The project weekly structure could be enhanced (maybe adding one more week could help) and the weekly instructions, while informative, could also be improved. Thankfully the forum has been very helpful. Informative and motivating videos but where were the SwiftKey people mentioned ? Finally, the quizzes 2 and 3 should be replaced by other exercises with more educational value. Overall an interesting learning opportunity !

Most of the courses were very well tought and contained useful material.

Thanks to all three instructors