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.
This course is part of the Applied Data Science with Python Specialization
Applied Text Mining in Python
Offered By
About this Course
4.2
1,319 ratings
What you will learn
Apply basic natural language processing methods
Describe the nltk framework for manipulating text
Understand how text is handled in Python
Write code that groups documents by topic
Skills you will gain
Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing
Syllabus - What you will learn from this course
Week
1Module 1: Working with Text in Python
5 videos (Total 56 min), 4 readings, 3 quizzes
5 videos
Handling Text in Python18m
Regular Expressions16m
Demonstration: Regex with Pandas and Named Groups5m
Internationalization and Issues with Non-ASCII Characters12m
4 readings
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
Resources: Common issues with free text10m
2 practice exercises
Practice Quiz8m
Module 1 Quiz12m
Week
2Module 2: Basic Natural Language Processing
3 videos (Total 36 min), 3 quizzes
3 videos
Basic NLP tasks with NLTK16m
Advanced NLP tasks with NLTK16m
2 practice exercises
Practice Quiz4m
Module 2 Quiz10m
Week
3Module 3: Classification of Text
7 videos (Total 94 min), 2 quizzes
7 videos
Identifying Features from Text8m
Naive Bayes Classifiers19m
Naive Bayes Variations4m
Support Vector Machines24m
Learning Text Classifiers in Python15m
Demonstration: Case Study - Sentiment Analysis9m
1 practice exercise
Module 3 Quiz14m
Week
4Module 4: Topic Modeling
4 videos (Total 58 min), 2 readings, 3 quizzes
4 videos
Topic Modeling8m
Generative Models and LDA13m
Information Extraction18m
2 readings
Additional Resources & Readings10m
Post-Course Survey10m
2 practice exercises
Practice Quiz4m
Module 4 Quiz10m
4.2
251 Reviews20%
started a new career after completing these courses
33%
got a tangible career benefit from this course
10%
got a pay increase or promotion
Top Reviews
By CC•Aug 27th 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 CB•Sep 20th 2017
Excellent course! Video lectures are high quality, with realistic problems and applications. Exercises are reasonably challenging, and all quite fun to do! Strongly recommend this course
Instructor
V. G. Vinod Vydiswaran
Assistant ProfessorSchool of Information
About 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.
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.
Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order. All 5 are required to earn a certificate.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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