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⇱ Applied Text Mining in Python | Coursera


Applied Text Mining in Python

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Applied Text Mining in Python

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Gain insight into a topic and learn the fundamentals.
4.2

3,824 reviews

Intermediate level
Some related experience required
Flexible schedule
3 weeks at 10 hours a week
Learn at your own pace
91%
Most learners liked this course

Gain insight into a topic and learn the fundamentals.
4.2

3,824 reviews

Intermediate level
Some related experience required
Flexible schedule
3 weeks at 10 hours a week
Learn at your own pace
91%
Most learners liked this course

What you'll 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

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

7 assignments

Taught in English

Build your subject-matter expertise

This course is part of the Applied Data Science with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 4 modules in this course

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.

What's included

5 videos4 readings2 assignments1 programming assignment1 discussion prompt2 ungraded labs

5 videosβ€’Total 56 minutes
  • Introduction to Text Miningβ€’4 minutes
  • Handling Text in Pythonβ€’19 minutes
  • Regular Expressionsβ€’17 minutes
  • Demonstration: Regex with Pandas and Named Groupsβ€’5 minutes
  • Internationalization and Issues with Non-ASCII Charactersβ€’12 minutes
4 readingsβ€’Total 40 minutes
  • Syllabusβ€’10 minutes
  • Help us learn more about you!!β€’10 minutes
  • Notice for Auditing Learners: Assignment Submissionβ€’10 minutes
  • Resources: Common issues with free textβ€’10 minutes
2 assignmentsβ€’Total 60 minutes
  • Practice Quizβ€’30 minutes
  • Module 1 Quizβ€’30 minutes
1 programming assignmentβ€’Total 180 minutes
  • Assignment 1β€’180 minutes
1 discussion promptβ€’Total 10 minutes
  • Introduce Yourselfβ€’10 minutes
2 ungraded labsβ€’Total 120 minutes
  • Working with Textβ€’60 minutes
  • Regex with Pandas and Named Groupsβ€’60 minutes

What's included

4 videos2 assignments1 programming assignment1 discussion prompt1 ungraded lab

4 videosβ€’Total 45 minutes
  • Basic Natural Language Processingβ€’4 minutes
  • Basic NLP tasks with NLTKβ€’17 minutes
  • Advanced NLP tasks with NLTKβ€’16 minutes
  • Application: Spell Checkerβ€’8 minutes
2 assignmentsβ€’Total 60 minutes
  • Practice Quizβ€’30 minutes
  • Module 2 Quizβ€’30 minutes
1 programming assignmentβ€’Total 180 minutes
  • Assignment 2β€’180 minutes
1 discussion promptβ€’Total 10 minutes
  • Finding your own prepositional phrase attachmentβ€’10 minutes
1 ungraded labβ€’Total 60 minutes
  • Module 2β€’60 minutes

What's included

7 videos1 assignment1 programming assignment1 ungraded lab

7 videosβ€’Total 94 minutes
  • Text Classificationβ€’12 minutes
  • Identifying Features from Textβ€’8 minutes
  • Naive Bayes Classifiersβ€’19 minutes
  • Naive Bayes Variationsβ€’5 minutes
  • Support Vector Machinesβ€’24 minutes
  • Learning Text Classifiers in Pythonβ€’15 minutes
  • Demonstration: Case Study - Sentiment Analysisβ€’10 minutes
1 assignmentβ€’Total 30 minutes
  • Module 3 Quizβ€’30 minutes
1 programming assignmentβ€’Total 180 minutes
  • Assignment 3β€’180 minutes
1 ungraded labβ€’Total 60 minutes
  • Case Study - Sentiment Analysisβ€’60 minutes

What's included

4 videos4 readings2 assignments1 programming assignment

4 videosβ€’Total 58 minutes
  • Semantic Text Similarityβ€’17 minutes
  • Topic Modelingβ€’8 minutes
  • Generative Models and LDAβ€’14 minutes
  • Information Extractionβ€’18 minutes
4 readingsβ€’Total 33 minutes
  • Additional Resources & Readingsβ€’10 minutes
  • Post-Course Surveyβ€’10 minutes
  • Keep Learning with Michigan Onlineβ€’10 minutes
  • Course 4 complete! βœ”οΈ Time to celebrateβ€’3 minutes
2 assignmentsβ€’Total 60 minutes
  • Practice Quizβ€’30 minutes
  • Module 4 Quizβ€’30 minutes
1 programming assignmentβ€’Total 180 minutes
  • Assignment 4β€’180 minutes

Earn a career certificate

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Instructor

Instructor ratings
4.0 (284 ratings)
University of Michigan
3 Coursesβ€’158,764 learners

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Learner reviews

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Showing 3 of 3824

RK
Β·

Reviewed on Jul 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.

AM
Β·

Reviewed on Aug 1, 2019

Excellent course for someone like me who is ambitious and aspires to gain knowledge on new things. The videos can be made bit more elaborate, seems to be rushing towards the end.

JR
Β·

Reviewed on Dec 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.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,