Applied Text Mining in Python
Applied Data Science with Python,
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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Rating | 3.7★ based on 572 ratings |
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Length | 5 weeks |
Starts | Jun 26 (44 weeks ago) |
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
machine learning
It was an order of magnitude times better than the previous course, 'Applied Machine Learning,' by Kevyn Collins-Thompson.
Fantastic I learned a lot about regular expressions, how to use NLTK to parse words and parts of speech, and to apply machine learning techniques from the third course to text.The homework assignments were finicky with the autograder and often there was a lot of frustration regarding the exact data types of the output.
Will appreciate if they can cover more content like the previous machine learning course.
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 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.
Understood Machine Learning as well.
For further learning, I discovered the NLP course in the Advanced Machine Learning specialization.
I am a Self Driving Car Engineer, I have worked with deep learning but i wanted to know about Machine Learning So i was exploring here.
Far superior to their machine learning course.
Overall, was a great course that was a good intro to the text machine learning tools in Python.
Repetition of content already introduced in previous courses, i.e., machine learning basics.
Nothing related to machine learning/using Python was discussed in the class (may be 2%).
I wish they emphasized on the machine learning more.
very powerful course for machine learning professionals.
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real world
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.
Could also use a more real world case study for the final project.
Really provides practical application and ideas on how to use tools in real world.
*Unlike other courses in the specialization, this one doesn't have good links to interesting academic papers or real world applications.
Nice, but first assignment shouldn't be considered here I think Assignment grading is way too rigid and not reflective of real world issues.
The assignments were a little complex 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.
Even the assignments didn't provide clarity on how the results are to be interpreted and what could be ther real world implications.
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applied data science
This course has restored my faith in the 'Applied Data Science with Python' specialization by University of Michigan and I am confident in my ability solve text classification problems in Python.
Of all of the Applied Data Science with Python classes I have taken, this was the worst.
Curriculum is valuable but the course quality isn't on par with the other Applied Data Science using Python courses by University of Michigan.
Would be good for everyone if this was removed from the (otherwise great) series "Applied Data Science in Python".
I have been working through the entire specialization, Applied Data Science with Python.
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university of michigan
These University of Michigan classes aren't very balanced in terms of lectures, reading, and difficulty of projects.
It is a terrible reflection on the University of Michigan.
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amount of time
My impression is that extremely complex concepts are mentioned in passsing and poorly explained, while a large amount of time is spent on trivial examples.
He spent a good amount of time building intuition behind the algorithms and techniques involved, and saved most of the coding for challenging and satisfying homework assignments--all qualities that the previous course did not have.
I have learned a lot, but last week had no tutorial example covering the topic and w4 assignment was not literally described resulting in spending a huge amount of time on trying which possible solutions will be accepted by autograder.
You would need to spend about the same amount of time googling how the packages work as I have never took the course.
There were a number of ambiguities and inaccuracies in the assignments that wasted a considerable amount of time for not just me but a lot of people - see the forums Great!
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auto grader
A lot of issues with the auto graders Great teaching material and clear explanations.
That being said, this is the first time I have taken a MOOC course and felt like 90% of the time I spent was fighting with the auto grader.
Too much of strange bugs with the auto grader.
But all the students faced issues during the assignment submissions because of the auto grader.
Nice Course, good learning Course is great except for the auto grader issues.
A lot of errors in auto graders, assignments.
tired of auto grader,...and prof not interested Excellent course.
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language processing
The topics of text mining and Natural Language Processing are central to data science, and deserve better instruction than this course delivered.
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.
This course give the basic idea in each module existed in text and natural language processing kits.
I am a Data Engineer with a degree in Computer Science who wanted to learn more about Natural Language Processing for a small project I wanted to build.
Natural language processing is an exciting field and I think there is a lot more potential to enthuse and engage students.
Assignments and resources are of great use Its still an elementary course for natural language processing .
Thank you for this amazing course on Natural Language Processing using NLTK Good Excellent course as an introduction to text processing using Python.
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learn text mining
Very good course to learn text mining!
it's great course to learn text mining in python , you will find many good examples which are related to real worlds problems Best instructor and teaching method Loved this course.
NICE COURSE AND A GOOD INTRODUCTION TO THE NLP ALGORITHMS IF you want to learn text mining this is the best place for your first step Interestng topics, well taught.
very good course to learn text mining basics using python Poorly constructed course overall.
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first three weeks
The assignments for the first three weeks were great in quality, and even though I had to spend some time on some 'unnecessary debugging ' due to their Autotrader every time I submitted my assignments, it actually was not that difficult to figure out.
I would recommend this course to others because the first three weeks' content was great and you could learn a ton from the first three weeks' assignments especially.
The first three weeks I really learned a lot, but the last week I don't fully understand the content.
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figure out
You need to spend hours browsing the discussion group just to figure out what is expected.
I have been doing some text mining in another tool, and I learned some useful things that I was able to put to use almost immediately ... now that I have the data science part in hand, I just need to figure out some Python details in order to format my output for my client.
Autograder is a disadvantage that sometimes can take many hours to figure out.
You have to spent days to figure out the right answer.
I would say it is more of a process to “try to figure out what the instructor is asking for”.
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discussion forums
However, the discussion forums are active and people are willing to give feedback!
Without the Discussion forums there is no way I would have ever figured out what to do for some parts of assignments.
For examples, please visit the discussion forums.
A lot of exercises have unclear instructions (see discussion forums).
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poorly worded
The autograder is frequently breaking for very minor things (such as returning numpy.float instead of float), the questions on the assignments are often misleading, poorly worded, vague, or just generally not very helpful.
Instructions in programming assignments are misleading or poorly worded.
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.
All 4 assignments are poorly worded in such a way that it's impossible to pass them without using the discussion forums.
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Careers
An overview of related careers and their average salaries in the US. Bars indicate income percentile.
Research Scientist-Machine Learning $55k
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Machine Learning Software Developer $103k
Software Engineer (Machine Learning) $116k
Applied Scientist, Machine Learning $130k
Autonomy and Machine Learning Solutions Architect $131k
Applied Scientist - Machine Learning -... $136k
RESEARCH SCIENTIST (MACHINE LEARNING) $147k
Machine Learning Engineer 2 $161k
Machine Learning Scientist Manager $170k
Machine Learning Scientist, Personalization $213k
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Rating | 3.7★ based on 572 ratings |
---|---|
Length | 5 weeks |
Starts | Jun 26 (44 weeks ago) |
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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