Ratings and Reviews for Applied Text Mining in Python

Graded assignments need more grounding in practically applicable situations.

Very painful going through this course although i have quite well coped with course 1-3. But this course seems lack of systematic structure of building the knowledge, it just walked through the topics quickly and extensively. I had to spend a few hours to learn about the whole structure of text mining to build in-depth knowledge, more than 20 hours to watch the online nltk & genism tutorials cause i m new to text mining & nlp. just hope the course can simplify the complicated topics such as where we are in the whole process, what's it, why we need it, working theory, coding, how we use these parameters, etc. to make life easier.

Noteworthy!

This course makes you give up on data science and MOOCs. Seriously, the content is poorly presented he keeps on speaking , telling 2-3 lines about a function and so on. I highly recommend stay away from this pathetic specialization.

One of the more disappointing classes in the U of M data science specialization, due mostly to inconsistent quality of the assignments. The videos are interesting but lacking in detail. The quizzes are trivial. Half of the assignments were OK but the other two were big time-wasters. The construction of this class seems just plain lazy. Proceed directly to google and skip this class.

I don't feel like I learned very much; even a month later, I've almost entirely forgotten what we covered. The homeworks were confusing and often poorly worded, and from what I saw from the forums, I wasn't the only one who was left baffled.

Nice course - but I found the assignment of week 1 a little to hard

So useful, the course provided enough fundamental knowledge with practice code in pandas and nltk.

The most discouraging course in specialization.

Lectures are good but the assignment of week1 and week 4 is a little bit absurd and unclarified. Autograder is too slow.