Applied Text Mining in Python
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Applied Text Mining in Python
This course is part of Applied Data Science with Python Specialization
Instructor: VG Vinod Vydiswaran
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3,824 reviews
3,824 reviews
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
Skills you'll gain
Tools you'll learn
Details to know
7 assignments
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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! βοΈ 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
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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.
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.
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.
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