V. G. Vinod Vydiswaran
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. The mission of the University of Michigan is to serve the people of Michigan and the world through preeminence in creating, communicating, preserving and applying knowledge, art, and academic values, and in developing leaders and citizens who will challenge the present and enrich the future.
Instructor Details
Courses : 1
Specification: Applied Text Mining in Python
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57 reviews for Applied Text Mining in Python
Add a review
| Price | Free |
|---|---|
| Provider | |
| Duration | 17 hours |
| Year | 2017 |
| Level | Intermediate |
| Language | English ... |
| Certificate | Yes |
| Quizzes | Yes |
FREE
Halil K –
Excellent course. Teaches useful knowledge and skills and does it with a very good teaching staff.
Carlos F P –
Autograder is a disadvantage that sometimes can take many hours to figure out. Also, this course was a let down compared to the previous in the specialization. I wish there were more examples.
Feng Q –
totally can’t understand the Indian accent.
Vinayak –
Well structured, awesome pedagogy and challenging assignments. It has all the elements it takes to make a MOOC epic! Thanks UMich and Coursera for helping put this course together and allowing me to pursue it.
Dongquan S –
I have taken and passed all the first four courses in this specialization, and very much liked the first three courses. But the quality of this course on text mining is far below the average level of the first three. Go find some other courses if you want to learn text mining with Python. There are too many areas of flaws in this course. I am only highlighting the top 5 below: 1. lacks good connection throughout the course content. This problem exists almost everywhere, both from slide to slide within a video and from video to video. Many times you would have questions in your head like “why is he talking about this?” or “what is this?” 2. use example just for the purpose of showing examples. Don’t really explain the point it is supposed to explain. In many times the examples do not provide clarity, but raise more confusion instead. 3. assignment tasks either too simple, or remotely related to what is introduced in the course. The worst case is assignment in week 4, where the assignment is so poorly constructed. You have to spent days to figure out the right answer. They call it “debug”, but there is nothing wrong with my code. I would say it is more of a process to “try to figure out what the instructor is asking for”. 4. talks too much about the theoretical things, not very good introduction of using python. Even when python code is demonstrated, it is almost always in a very abstract way. This is significantly different from the first three courses, and very annoying. You would need to spend about the same amount of time googling how the packages work as I have never took the course. 5. Repetition of content already introduced in previous courses, i.e., machine learning basics.
Amit B H –
The course wasn’t totally bad but it definitely wasn’t as good as the first three. I felt I was thrown in with insufficient tools to cope with the assignments. Relying on the internet is important but in these cases, you have to rely on it quite heavily. On assignments 1 and 3 in particular, Upon final submitting, I felt I didn’t learn much at all. Specifically with regexs, I feel extremely insecure with my regex skills and that is an understatement. I don’t think that is something that should happen after a text mining course. The following remark *isn’t* a crucial one: For a non native English speaker understanding the language could sometimes pose an obstacle. Now, decoding the lecturer’s accent is yet another obstacle on top of the former. Lecturer with an American accent will obviously be the best choice.
carol a –
Instructions for assignments are vague and incorrect. Instructor was hard to follow during lecture.
Shiomar S C –
Really good course… The teacher is great
Kangqiao –
A little bit stretched my python skill, but learned a lot. Forum is a good place, and maybe next I will join some study group online or offline to have more discussions.
Chen G –
Helpful..
Steven G –
Confusing explanations of NLP concepts. Inadequate explanations of how to use the Python packages to solve the assignment questions. I’m writing this review half way through the Applied Social Networks Analysis course which is excellent and pitched just right. The contrast between the 2 courses couldn’t be greater.
Angertdev S –
broken assignments
Mike W –
Compared to other courses, there’s a disconnect between what’s covered in the lecture and what’s needed to complete the assignments; the lectures at times have a more theoretical flavor. For a course with “applied” in the name, that’s a more significant mistake.
Emanuele G –
Excellent course
Yonatan S –
A lot of exercises have unclear instructions (see discussion forums). The exercise on topic modeling especially was a waste of time, you’re not really learning anything by running these small pre frabricated scripts. In general the exercises were extremely shallow and did not require any creativity or actual problem solving, in contrast to some of the earlier courses in this Specialization.
Alex W –
Really poor instructions on week 4. Overall, was a great course that was a good intro to the text machine learning tools in Python.
Anand N A –
This course contents was good, but the assessment was really bad. You guys need to fix the autograder issues ASAP and I feel the instructor was not taking as much care as others to set the autograder propertly. Lot of time was unnecessarily wasted I would say as the instructions could have been better. Very disappointed with the assessment. Course is very useful and valuable.
Jahir M –
it gives you the right things to start making models to extract information without getting too technical.
Wenlei Y –
This course compared with the others in this specialization, is not as well organized. You might have to spend lots of time working on the assignments by yourself (i.e. you cannot find related guidance in the course materials); There is less helpful online information, compared to course 1 3 in this specialization, either so it is a little painful to do these assignments. However, the tools and the theories behind them are useful and powerful. If you are really interested in text mining, you will benefit a lot! The instructor is passionate and humorous.
Timothy P –
Challenging but fun class, I learned a lot. Much to build on and keep learning about. Thanks
Patrick L –
It needs update
S S –
Good course, but not up to date in current scenario
Vivek G –
Only useful for coarse understanding of the topic.
Abdul K S –
Very nice and interactive course learnt a lot and the assignments are very good and very importing topics covered.
Nishal –
The explanations weren’t the best and pacing wasn’t amazing, but some good ground covered and parts were interesting.
Matthew O –
I found this course the least valuable of the courses in the specialisation so far. The video content wasn’t quite as slick/informative, the assignments not quite as useful or well worded, making them ambiguous in a few places and generally it just wasn’t quite as good. Not terrible, but just not quite up to the high standards of the other courses so far.
Praveen R –
I learnt about NLTK package and its capabilities. It was good to know how to build vocabulary and guess missing words and match sentences lemmatizing them. Good eye opener course. There is way much more to be learnt in this subject. This is just an introduction (a good one).
Haris P D –
An really amazing course. The experience I had from this course that was unique!
Anubhav M –
I will give it a 4 star because of the assignments. The lectures were good but were a bit short.
Hieu N –
Thorough explanations provided by the lecturer and assignments which are challenging but helpful for learning purposes
Shanaka C –
Learned lot of text mining by studying this course
Mahmoud R –
AWESOME!
Benjamin C –
Good course overall but necessary upadates are lacking.
Yusheng F –
Great
Talha I –
An excellent course for beginners to enter the text mining practically.
Vinit D –
Excellent course
davide g –
A very good course
David K –
An interesting topic that takes text mining to a new level, it was really insightful to understand how these tools can be applied to the real world.
Navjyot W –
The assignments were a little complex
Asad M –
Course is good but doesn’t cover the whole lot of topics. the instructor is very good and clear. I expected to learn more advanced topics though.
Agata M –
Well prepared and put in an interesting way. Assignments allowed me to get general understanding of the techniques.
MUHAMMAD M M –
good!
Joseph I –
The videos and content were great but the projects need more specificity. There’s a lot of ambiguity around what the projects are asking for which takes away from the quality of the course. For examples, please visit the discussion forums.
chris l –
A lot of prior knowledge or independent learning is required to get the most out of this course. Needs more code walkthroughs.
BrajKishore P –
The overall course was well designed, all lectures were arranged in a proper sequence and all the slides and jupyter notebooks were good covering all the aspects, but I felt some difficulties in the 2nd week in POS tag, overall it was too good.
Xuening H –
Bad autograder
Raghavendra N –
Good one, bring in more industry relevance in the course
peyman s –
This course offers a good package of skills with great notebooks (except week 4) and assignments. The videos could be a lot better but mostly understandable. You can always search Youtube for better explained videos.
Kerem Y –
I liked the previous courses in the series better. I think this course did not have enough “meat on the bones”; the ML method descriptions were generic and already seen in previous courses. Would have liked seeing more explanation how this all works in context of text and text mining etc
akash p –
it was helpful
Tatek K –
Excellent presentation, exercise and reading materials. Thank you
Akash D –
Thank You! Sir
Akshat S –
The NLTK library was not explained properly. No code explanation was provided.
Bruce M –
Great course!
Dan H –
There were significant issues with the autograder and the instructions for the programming assignments. This course has been around for a while. Why aren’t they fixed???
Su L –
love it
Xin Y –
Excellent course! Thank you!