Course Summary
Learn how to use Python for text mining, including techniques for data acquisition, pre-processing, exploration, and visualization. This course will also cover machine learning algorithms and models for text mining.Key Learning Points
- Learn how to use Python for text mining
- Explore techniques for data acquisition and pre-processing
- Discover machine learning algorithms for text mining
Related Topics for further study
Learning Outcomes
- Understand the basics of text mining with Python
- Acquire and preprocess text data for analysis
- Apply machine learning algorithms to text mining tasks
Prerequisites or good to have knowledge before taking this course
- Basic understanding of Python programming
- Familiarity with data analysis concepts
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Text Mining in Python
- Text Mining and Analytics
Notable People in This Field
- Jacob Perkins
- Sebastian Raschka
Related Books
Description
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).
Knowledge
- 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
Outline
- Module 1: Working with Text in Python
- Introduction to Text Mining
- Handling Text in Python
- Regular Expressions
- Demonstration: Regex with Pandas and Named Groups
- Internationalization and Issues with Non-ASCII Characters
- Course Syllabus
- Help us learn more about you!!
- Notice for Auditing Learners: Assignment Submission
- Resources: Common issues with free text
- Practice Quiz
- Module 1 Quiz
- Module 2: Basic Natural Language Processing
- Basic Natural Language Processing
- Basic NLP tasks with NLTK
- Advanced NLP tasks with NLTK
- Practice Quiz
- Module 2 Quiz
- Module 3: Classification of Text
- Text Classification
- Identifying Features from Text
- Naive Bayes Classifiers
- Naive Bayes Variations
- Support Vector Machines
- Learning Text Classifiers in Python
- Demonstration: Case Study - Sentiment Analysis
- Module 3 Quiz
- Module 4: Topic Modeling
- Semantic Text Similarity
- Topic Modeling
- Generative Models and LDA
- Information Extraction
- Additional Resources & Readings
- Post-Course Survey
- Keep Learning with Michigan Online
- Practice Quiz
- Module 4 Quiz
Summary of User Reviews
Learn Python Text Mining on Coursera and improve your skills in natural language processing, data mining, and machine learning. This course has received positive reviews from learners who appreciated the practical approach and the comprehensive curriculum.Key Aspect Users Liked About This Course
Many users found the hands-on assignments and real-life examples to be extremely helpful in understanding the concepts.Pros from User Reviews
- Great course for beginners and experienced learners.
- Very well-structured curriculum with practical applications.
- Instructors provide clear explanations and are highly responsive to questions.
- Great community support from fellow learners and instructors.
- Course content is updated regularly to reflect current trends and technologies.
Cons from User Reviews
- Some learners found the pace of the course to be too fast.
- A few users felt that the assignments were too difficult and time-consuming.
- The course requires some prior knowledge of Python programming.
- Some learners found the lectures to be too basic and lacking in depth.
- The course may not be suitable for learners who are looking for a theoretical approach to text mining.
V. G. Vinod Vydiswaran
4.2 Raiting
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