Applied Text Mining in Python

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data.Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representat

( 384 Reviews )

Created by: V. G. Vinod Vydiswaran

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Overall Score : 72 / 100

Course Description

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.

Time: 17h

Instructor Details

V. G. Vinod Vydiswaran

3.6 Rating
384 Reviews

V. G. Vinod Vydiswaran is an Assistant Professor of Learning Health Sciences, Medical School and Assistant Professor of Information, School of Information at the University of Michigan. His research interests are primarily in information trustworthiness, large-scale text mining and analysis, and natural language processing as well as data mining, information extraction, machine learning, building learning health systems, and working on interesting applications of algorithmic models to address real world challenges. His current research focuses on mining and analyzing health information from multiple sources, including scientific literature, community health forums, and social and information networks. He is specifically interested in analyzing online medical textual information to infer credibility of sources and the claims they make.

Reviews

3.6

384 total reviews

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By Ayush T on 23-Aug-17

Very good course with very good material and teachers. I just missed some more practical examples to follow along the classes, and more further readings (specially for information extraction).

By Ting-Shuo Y on 1-Sep-17

Overall, a solid course, though it felt a bit like a face-to-face lecture course recorded to video. 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.

By Sitie F A on 23-Aug-17

Honestly, I was pretty disappointed in this course. Assignments consistently took much longer than indicated, in large part because of recurrent problems with the autograder and unspecified requirements in assignment instructions.

By Yang L on 17-Oct-17

The professor is wooden. The quizzes are ridiculously easy. The programming assignments nearly impossible. Beware the hidden workings of the auto-grader. If you're very lucky, one of the other students will prompt the TAs to action in the forums. This is, by far, the worst course in this specialization.

By Jean-Claude R on 13-Apr-18

The professor needs to prepare students better for exams and assigments. Too few lectures.

By Lucas V on 23-Mar-19

Este curso no vale para nada, por favor no lo hagais!!!

By SHIVAKANTH C on 30-Jul-18

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.

By Tal Y on 18-Feb-18

The course takes you through the important NLP topics, the instruction is decent, but the assignments are clunky and waisted many hours of my time unproductively.

By Steve M on 3-May-18

The content of this course has great potential, but needs significant refinement. The lectures, while delivered with enthusiasm, were very theoretical/academic and provided little in the way of preparation for the more practical exercises. The disconnect between lectures and assignments, coupled with technical challenges (autograder glitches) were frustrating. The only support came from one dedicated volunteer Coursera Mentor; the instructor cadre was absent or unavailable to students throughout the four week period. The topics of text mining and Natural Language Processing are central to data science, and deserve better instruction than this course delivered.

By Ashwini B on 3-Jun-18

Topics like LDA need better explanations.

By Max P on 6-Jan-18

Although the topic of Text Mining is very interesting, I find that the AP did not dive deep enough into the various topics. The matter that he did explain was interesting, but at some parts not really clear. I missed a clear line of thought.Concerning the assignments: very interesting topics, but the guidelines could be clarified to nip any possible confusion in the bud. Also, some exercises could be split up into multiple ones so that debugging becomes easy. Many students in the Discussion Forum mentioned difficulties.

By Thomas B on 22-Apr-18