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Applied Text Mining in Python

This course is a part of Applied Data Science with Python, a 5-course Specialization series from Coursera.

This course will introduce the learner to text mining and text manipulation basics. The course begins with an understanding of how text is handled by python, the structure of text both to the machine and to humans, and an overview of the nltk framework for manipulating text. The second week focuses on common manipulation needs, including regular expressions (searching for text), cleaning text, and preparing text for use by machine learning processes. The third week will apply basic natural language processing methods to text, and demonstrate how text classification is accomplished. The final week will explore more advanced methods for detecting the topics in documents and grouping them by similarity (topic modelling). This course should be taken after: Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, and Applied Machine Learning in Python.

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University of Michigan

Rating 3.5 based on 168 ratings
Length 5 weeks
Starts May 6 (next week)
Cost $79
From University of Michigan via Coursera
Instructors Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, V. G. Vinod Vydiswaran
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

text mining in 27 reviews

Exceptional!I was using up to now strictly regular expressions for text mining, and that was a headache.

I strongly recommend it to any one that wants to use ML to study texts When looking at the full course in coursera, I was thinking that would be the course which would interest me the least, but at it turned out, now I'm really interested in text mining, and I'm planning to read more publication to understand that field I like the lecturer.

The topics of text mining and Natural Language Processing are central to data science, and deserve better instruction than this course delivered.

As a result this is a must have course for text mining but I think that the level is introductory and in real world one must have more skills to perform a respected text mining.

Excellent introduction to practical text mining in Python.

I learned the basic concepts and skills of text mining.

Overall a good course and a nice introduction to Text Mining but issues with the autograder and some unclear instructions can make the assignments a little painful.

Well taught the text mining classes.

very good in 10 reviews

Some rather vague assignments instructions, some assignments require material only briefly mentioned in lectures Very good overview of the NLP tasks.

Maybe it lacks of a practical activity in Week 4 before the assessment, but overall the course has very good content and an excellent instructor This course provides an interesting introduction to natural language processing in Python.

Excellent material The videos are very good, the teacher is clear and concepts are explained well.

Very good course.

A little bit heavy on in-built functions which hides what is happening underneath, but overall very good.

Very good start for Beginners Good course, teaches you some basic concepts and skills about text mining using Python.

The presentation of the instructor is very good.

very good course that taught me a lot!

other courses in 7 reviews

In summary, the assignments' descriptions and grading system do need to be improved (for example, one can introduce some hints such as 'the grader expected this output for this input0, but the student solution returned this' as it is done in a few other courses on Coursera).

Too coarse, quality worse than other courses in this specialization.

The programming assignments are more interesting and appropriately challenging (compared to other courses in the specialization), but leave me without any confidence that I could accomplish a text mining task in python independently.

Compared to other courses in the Applied Machine Learning focus, this is so far the worst.

One criticism is that the general quality of notebooks provided with example codes wasn't as high as for other courses in the specialization.However the lecturer was really nice and gave very good explanations even for complicated concepts.

The material was helpful and well-explained, but I feel it could benefit from taking advantage of the MOOC medium more effectively, such as by providing code sample notebooks for the students to run and modify, which have been very helpful to me in understanding the material in other courses in the same specialization.

However comparing to the other courses there is much talk from the lecturer and not so much of interesting background information of this topic.

programming assignments in 6 reviews

The course is quite interesting and you learn the basic concepts and tools.The programming assignments were sometimes unclear in the formulation of the tasks.

Instructions in programming assignments are misleading or poorly worded.

The programming assignments nearly impossible.

The programming assignments are very interesting.

IMO, the lectures were at much too high a level while the programming assignments were very detailed with vague instructions and little guidance.

I would also recommend using Professor Andrew Ngs Machine Learning course as a guide for how to create great programming assignments, with detailed PDFs (typically 5-6 pages) describing what is to be done AND WHY (linking back to the lectures) and "telling a story" that is cohesive and leads the student to create something end-to-end (in small steps) that does something amazing by the end.

The programming assignments in this course seemed, in contrast, to be a shotgun blast of "do this", "create this", "make this happen" with little context of how the small pieces fit together or what the overall goal of the assignment is to accomplish -- and at the end, a feeling of "I passed the autograder's expections, but have no idea what I've really done or why".

more practical in 6 reviews

The lectures, while delivered with enthusiasm, were very theoretical/academic and provided little in the way of preparation for the more practical exercises.

I really think that the 3rd and 4th week of the course should have more practical presentation (especially the 4th week for which the assignment is quite 'new' in terms of programming).

I would have like to have more practical tutorials.

Great course, could improve the last week with more practical examples.

I just missed some more practical examples to follow along the classes, and more further readings (specially for information extraction).

The topic is interesting, however as with the Machine Learning course from UM, this one suffers from too much theoretically focused graded assignments, and would benefit from more practical real life example tasks.


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University of Michigan

Rating 3.5 based on 168 ratings
Length 5 weeks
Starts May 6 (next week)
Cost $79
From University of Michigan via Coursera
Instructors Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, V. G. Vinod Vydiswaran
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Data Analysis Software Development

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