This course is part of the Applied Data Science with Python Specialization

4.2
stars
3,645 ratings

V. G. Vinod Vydiswaran

124,695 already enrolled

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Applied Data Science with Python SpecializationUniversity of Michigan

About this Course

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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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Course 4 of 5 in the

Applied Data Science with Python Specialization

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 Mining
Python Programming
Natural Language Processing

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 4 of 5 in the

Applied Data Science with Python Specialization

Intermediate Level

Approx. 29 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish

Instructor

Instructor rating

4.06/5 (236 Ratings)

V. G. Vinod Vydiswaran

Assistant Professor

School of Information

124,695 Learners

1 Course

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

Content Rating92%(6,504 ratings)

Week

Week 1

9 hours to complete

Module 1: Working with Text in Python

9 hours to complete

5 videos (Total 56 min), 4 readings, 3 quizzes

Week

Week 2

7 hours to complete

Module 2: Basic Natural Language Processing

7 hours to complete

3 videos (Total 36 min)

Week

Week 3

7 hours to complete

Module 3: Classification of Text

7 hours to complete

7 videos (Total 94 min)

Week

Week 4

6 hours to complete

Module 4: Topic Modeling

6 hours to complete

4 videos (Total 58 min), 3 readings, 3 quizzes

Reviews

4.2

712 reviews

5 stars
55.07%
4 stars
25.23%
3 stars
12.01%
2 stars
4.34%
1 star
3.33%

TOP REVIEWS FROM APPLIED TEXT MINING IN PYTHON

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 BKJun 25, 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.

by TSJul 31, 2019

Good course to take although I felt the course could have been better in terms of practice. But overall, would recommend to others if they wish to pursue data analysis.

by KNov 3, 2019

A little bit stretched my python skill, but learned a lot. Forum is a good place, and maybe next I will join some study group online or offline to have more discussions.

View all reviews

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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.
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.
Is financial aid available?
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.

More questions? Visit the Learner Help Center.

Applied Text Mining in Python