About this Course
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100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 17 hours to complete

Suggested: 9 hours/week...

English

Subtitles: English, Korean

What you will learn

  • Check

    Apply basic natural language processing methods

  • Check

    Describe the nltk framework for manipulating text

  • Check

    Understand how text is handled in Python

  • Check

    Write code that groups documents by topic

Skills you will gain

Natural Language Toolkit (NLTK)Text MiningPython ProgrammingNatural Language Processing

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 17 hours to complete

Suggested: 9 hours/week...

English

Subtitles: English, Korean

Learners taking this Course are

  • Data Scientists
  • Data Analysts
  • Machine Learning Engineers
  • Data Engineers
  • User Experience Researchers

Syllabus - What you will learn from this course

Week
1
8 hours to complete

Module 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
2
6 hours to complete

Module 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
3
7 hours to complete

Module 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
4
6 hours to complete

Module 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
379 ReviewsChevron Right

31%

started a new career after completing these courses

33%

got a tangible career benefit from this course

Top reviews from Applied Text Mining in Python

By GKMay 4th 2019

Lectures are very good with a perfect explanation. More than lectures I liked the assignment questions. They are worth doing. You will get to know the basic foundation of text mining. :-)

By BKJun 26th 2018

Would love to see these courses have more practice questions in each weeks lesson. Would be helpful for repetition sake, and learning vs only doing each question once in the assignments.

Instructor

Avatar

V. G. Vinod Vydiswaran

Assistant Professor
School 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....
Applied Data Science with Python

Frequently Asked Questions

  • 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.

  • 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.

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