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 is part of the Applied Data Science with Python Specialization
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Applied Data Science with Python SpecializationUniversity of MichiganAbout this Course
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Learner Career Outcomes
29%
started a new career after completing these courses
32%
got a tangible career benefit from this course
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Earn a Certificate upon completion
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Course 4 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 29 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
What you will learn
Understand how text is handled in Python
Apply basic natural language processing methods
Write code that groups documents by topic
Describe the nltk framework for manipulating text
Skills you will gain
Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing
Learner Career Outcomes
29%
started a new career after completing these courses
32%
got a tangible career benefit from this course
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 4 of 5 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
Approx. 29 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Offered by
University of Michigan
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.
Syllabus - What you will learn from this course
9 hours to complete
Module 1: Working with Text in Python
9 hours to complete
5 videos (Total 56 min), 4 readings, 3 quizzes
7 hours to complete
Module 2: Basic Natural Language Processing
7 hours to complete
3 videos (Total 36 min)
7 hours to complete
Module 3: Classification of Text
7 hours to complete
7 videos (Total 94 min)
6 hours to complete
Module 4: Topic Modeling
6 hours to complete
4 videos (Total 58 min), 3 readings, 3 quizzes
Reviews
TOP REVIEWS FROM APPLIED TEXT MINING IN PYTHON
by RKJul 19, 2019
Course is great except for the auto grader issues. Please look into the issue. I would like to take this opportunity and thank Prof V. G. Vinod Vydiswaran and all those who helped me to complete it.
by JRDec 4, 2020
Excellent course to get started with text mining and NLP with Python. The course goes over the most essential elements involved with dealing with free text. Definitely worth the time I spent on it.
by CCAug 26, 2017
Quite challenging but also quite a sense of accomplishment when you finish the course. I learned a lot and think this was the course I preferred of the entire specialization. I highly recommend it!
by LCFeb 16, 2018
Love the focus on conceptual text processing and practical guides to implementation in python, but the assignment grader was extremely specific for no reason, especially the Week3 assignment.
About the Applied Data Science with Python Specialization
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.
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