_:nc7d332d27fec4707bc00f37f99916640xb39
<http://schema.org/reviewBody>
"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. \n \nI 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. \n \nLooking 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. \n \nThe 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. \n \nStatistical 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. \n \nThe 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. \n \nImplementing the model in shiny and then adjusting it because it was very heavy was also interesting. \n \nAs 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."
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