About this Course
4.2
1,171 ratings
227 reviews
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....
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Intermediate Level

Intermediate Level

Clock

Approx. 17 hours to complete

Suggested: 8 hours/week...
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English

Subtitles: English...

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
Globe

100% online courses

Start instantly and learn at your own schedule.
Calendar

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Clock

Approx. 17 hours to complete

Suggested: 8 hours/week...
Comment Dots

English

Subtitles: English...

Syllabus - What you will learn from this course

Week
1
Clock
8 hours to complete

Module 1: Working with Text in Python

...
Reading
5 videos (Total 56 min), 4 readings, 3 quizzes
Video5 videos
Handling Text in Python18m
Regular Expressions16m
Demonstration: Regex with Pandas and Named Groups5m
Internationalization and Issues with Non-ASCII Characters12m
Reading4 readings
Course Syllabus10m
Help us learn more about you!10m
Notice for Auditing Learners: Assignment Submission10m
Resources: Common issues with free text10m
Quiz2 practice exercises
Practice Quiz8m
Module 1 Quiz12m
Week
2
Clock
6 hours to complete

Module 2: Basic Natural Language Processing

...
Reading
3 videos (Total 36 min), 3 quizzes
Video3 videos
Basic NLP tasks with NLTK16m
Advanced NLP tasks with NLTK16m
Quiz2 practice exercises
Practice Quiz4m
Module 2 Quiz10m
Week
3
Clock
7 hours to complete

Module 3: Classification of Text

...
Reading
7 videos (Total 94 min), 2 quizzes
Video7 videos
Identifying Features from Text8m
Naive Bayes Classifiers19m
Naive Bayes Variations4m
Support Vector Machines24m
Learning Text Classifiers in Python15m
Demonstration: Case Study - Sentiment Analysis9m
Quiz1 practice exercise
Module 3 Quiz14m
Week
4
Clock
6 hours to complete

Module 4: Topic Modeling

...
Reading
4 videos (Total 58 min), 2 readings, 3 quizzes
Video4 videos
Topic Modeling8m
Generative Models and LDA13m
Information Extraction18m
Reading2 readings
Additional Resources & Readings10m
Post-Course Survey10m
Quiz2 practice exercises
Practice Quiz4m
Module 4 Quiz10m
4.2
Direction Signs

20%

started a new career after completing these courses
Briefcase

83%

got a tangible career benefit from this course
Money

10%

got a pay increase or promotion

Top Reviews

By CCAug 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 CBSep 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 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.

More questions? Visit the Learner Help Center.