Applied Text Mining in Python

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Description

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About 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…

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About 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.

Who is this class for: This course is part of “Applied Data Science with Python“ and is intended for learners who have basic python or programming background, and want to apply statistics, machine learning, information visualization, social network analysis, and text analysis techniques to gain new insight into data. Only minimal statistics background is expected, and the first course contains a refresh of these basic concepts. There are no geographic restrictions. Learners with a formal training in Computer Science but without formal training in data science will still find the skills they acquire in these courses valuable in their studies and careers.

Created by:  University of Michigan
  • Taught by:  V. G. Vinod Vydiswaran, Assistant Professor

    School of Information
Basic Info Course 4 of 5 in the Applied Data Science with Python Specialization Level Intermediate Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.1 stars Average User Rating 4.1See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Syllabus


WEEK 1


Module 1: Working with Text in Python



5 videos, 3 readings, 1 practice quiz expand


  1. Reading: Course Syllabus
  2. Reading: Help us learn more about you!
  3. Video: Introduction to Text Mining
  4. Video: Handling Text in Python
  5. Notebook: Working with Text
  6. Video: Regular Expressions
  7. Notebook: Regex with Pandas and Named Groups
  8. Video: Demonstration: Regex with Pandas and Named Groups
  9. Practice Quiz: Practice Quiz
  10. Video: Internationalization and Issues with Non-ASCII Characters
  11. Discussion Prompt: Introduce Yourself
  12. Reading: Resources: Common issues with free text
  13. Notebook: Assignment 1

Graded: Module 1 Quiz
Graded: Assignment 1 Submission

WEEK 2


Module 2: Basic Natural Language Processing



3 videos, 1 practice quiz expand


  1. Video: Basic Natural Language Processing
  2. Notebook: Module 2 (Python 3)
  3. Video: Basic NLP tasks with NLTK
  4. Video: Advanced NLP tasks with NLTK
  5. Practice Quiz: Practice Quiz
  6. Discussion Prompt: Finding your own prepositional phrase attachment
  7. Notebook: Assignment 2

Graded: Module 2 Quiz
Graded: Assignment 2 Submission

WEEK 3


Module 3: Classification of Text



7 videos expand


  1. Video: Text Classification
  2. Video: Identifying Features from Text
  3. Video: Naive Bayes Classifiers
  4. Video: Naive Bayes Variations
  5. Video: Support Vector Machines
  6. Video: Learning Text Classifiers in Python
  7. Notebook: Case Study - Sentiment Analysis
  8. Video: Demonstration: Case Study - Sentiment Analysis
  9. Notebook: Assignment 3

Graded: Module 3 Quiz
Graded: Assignment 3 Submission

WEEK 4


Module 4: Topic Modeling



4 videos, 2 readings, 1 practice quiz expand


  1. Video: Semantic Text Similarity
  2. Video: Topic Modeling
  3. Video: Generative Models and LDA
  4. Practice Quiz: Practice Quiz
  5. Video: Information Extraction
  6. Reading: Additional Resources & Readings
  7. Notebook: Assignment 4
  8. Reading: Post-Course Survey

Graded: Module 4 Quiz
Graded: Assignment 4 Submission
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There are no frequently asked questions yet. Send an Email to info@springest.com