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V. G. Vinod Vydiswaran
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).
Read more
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).
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/1mfgn0/applied
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What's inside
Syllabus
Module 1: Working with Text in Python
Module 2: Basic Natural Language Processing
Module 3: Classification of Text
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Syllabus
Module 1: Working with Text in Python
Module 2: Basic Natural Language Processing
Module 3: Classification of Text
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces techniques for manipulating and classifying text, which are foundational skills for many different domains, ranging from data science to engineering, and creative writing
Covers basic natural language processing methods that provides learners with a solid foundation in this growing and impactful area of study
Lays a strong foundation for further study of more advanced natural language processing methods
Taught by instructors V. G. Vinod Vydiswaran, recognized for expertise in data mining
Requires prior knowledge of Python, plotting, charting, data representation, and applied machine learning
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Reviews summary
Practical text mining & nlp fundamentals
Learners say this course provides a solid and practical foundation in text mining and NLP, especially for those with prior Python and machine learning knowledge. The instructor's clear explanations and well-structured modules make complex topics accessible. Many appreciate the hands-on coding exercises and useful Jupyter notebooks which enhance learning. While some older feedback noted a lack of depth in advanced topics or felt it was initially too theoretical, recent updates, including new content on pre-trained models, have significantly enhanced its relevance and practical application. Some found certain sections, like topic modeling, a bit rushed.
Experience affects perception of course difficulty and depth.
"As someone with a strong machine learning background but new to text processing, this course was a mixed bag. The initial modules were too basic for me..."
"I had some prior experience, and found it a good refresher and a way to learn new techniques."
"I found it wasn't challenging enough or practical enough for my needs as a data scientist."
Requires prior Python, data science, and ML knowledge.
"Ensure you're comfortable with the prerequisites; it's definitely not for absolute beginners."
"For someone completely new, the pace might be a bit fast without prior knowledge. The course prerequisites are crucial."
"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."
Instructor explains concepts clearly, modules are well-organized.
"Module 2 on basic NLP and Module 3 on classification were particularly well-structured."
"The lectures are clear, and the instructor explains concepts well. The provided notebooks are very helpful."
"The instructor is excellent, breaking down complex ideas into digestible chunks. The progression of topics is very well thought out."
Focuses on hands-on coding and real-world application.
"This course truly bridges the gap between theory and practical application. Highly recommend for anyone with a good Python background."
"The emphasis on practical application through coding exercises is excellent. I learned a lot about regular expressions and text cleaning."
"It demystifies text mining concepts and provides practical Python implementations. I enjoyed the hands-on coding challenges and found them very effective for learning."
Recent updates enhance relevance and address past concerns.
"Just completed this course and found it incredibly valuable. The updated notebooks and explanations are much clearer than I remember from older versions."
"The instructor's recent additions on pre-trained models are a game-changer, making it highly relevant for current NLP tasks."
"I appreciate how the instructor continues to refine the content based on feedback, addressing earlier critiques on depth."
Some advanced sections could benefit from deeper coverage.
"My only minor critique is that some parts felt a bit rushed, especially the topic modeling section."
"Some sections could benefit from deeper dives or more challenging exercises for intermediate learners."
"The NLP and topic modeling sections felt like a high-level overview rather than an applied deep dive."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Applied Text Mining in Python with these
activities:
Volunteer for a Text Annotation Project
Show steps
Gain practical experience while contributing to the NLP community by annotating text data for research or industry projects.
Browse courses on
Natural Language Processing
Show steps
-
Search for volunteer opportunities on platforms like Kaggle or Zooniverse.
-
Follow the project guidelines and contribute high-quality annotations.
Compile a Resource List for Text Mining
Show steps
Improve your access to valuable resources by compiling a centralized list of tools, articles, and tutorials related to text mining.
Browse courses on
Text Mining
Show steps
-
Search and gather resources from various sources, such as academic databases, industry blogs, and online repositories.
-
Organize and categorize the resources into a structured document or spreadsheet.
Participate in a Study Group for Text Mining
Show steps
Collaborate with peers to discuss text mining concepts, share ideas, and enhance your understanding.
Browse courses on
Text Mining
Show steps
-
Form a study group with classmates or online learners.
-
Regularly meet to discuss course materials, solve problems, and engage in critical analysis.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
Show steps
Review the fundamentals of NLTK to strengthen your understanding of text manipulation and natural language processing.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read and summarize the introduction and first chapter.
-
Complete the exercises at the end of each chapter, focusing on applying NLTK functions.
Follow Tutorials on Natural Language Processing Applications
Show steps
Expand your knowledge by exploring practical applications of natural language processing and text mining.
Browse courses on
Natural Language Processing
Show steps
-
Search for tutorials on topics such as sentiment analysis, spam detection, or named entity recognition.
-
Follow the tutorials, implement the techniques, and evaluate your results.
Practice Regular Expression Exercises
Show steps
Sharpen your regular expression skills, essential for text manipulation and search operations.
Browse courses on
Regular Expressions
Show steps
-
Solve practice problems on RegExr or Regex101.
-
Apply your skills to a text cleaning task, such as removing punctuation from a dataset.
Attend a Workshop on Natural Language Processing Tools
Show steps
Enhance your skills by attending a workshop that provides hands-on experience with popular NLP tools and techniques.
Browse courses on
Natural Language Processing
Show steps
-
Research and identify relevant workshops offered by universities, industry organizations, or online platforms.
-
Register for a workshop that aligns with your learning goals.
Create a Topic Modeling Visualization
Show steps
Reinforce your understanding of topic modeling by visualizing the topics identified in a text corpus.
Browse courses on
Topic Modeling
Show steps
-
Use a library like gensim or scikit-learn to perform topic modeling on a dataset.
-
Create a visualization of the topics using techniques such as word clouds or interactive plots.
Volunteer for a Text Annotation Project
Show steps
Gain practical experience while contributing to the NLP community by annotating text data for research or industry projects.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Search for volunteer opportunities on platforms like Kaggle or Zooniverse.
- Follow the project guidelines and contribute high-quality annotations.
Compile a Resource List for Text Mining
Show steps
Improve your access to valuable resources by compiling a centralized list of tools, articles, and tutorials related to text mining.
Browse courses on
Text Mining
Show steps
Show steps
Show steps
- Search and gather resources from various sources, such as academic databases, industry blogs, and online repositories.
- Organize and categorize the resources into a structured document or spreadsheet.
Participate in a Study Group for Text Mining
Show steps
Collaborate with peers to discuss text mining concepts, share ideas, and enhance your understanding.
Browse courses on
Text Mining
Show steps
Show steps
Show steps
- Form a study group with classmates or online learners.
- Regularly meet to discuss course materials, solve problems, and engage in critical analysis.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Read Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
Show steps
Review the fundamentals of NLTK to strengthen your understanding of text manipulation and natural language processing.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read and summarize the introduction and first chapter.
- Complete the exercises at the end of each chapter, focusing on applying NLTK functions.
Follow Tutorials on Natural Language Processing Applications
Show steps
Expand your knowledge by exploring practical applications of natural language processing and text mining.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Search for tutorials on topics such as sentiment analysis, spam detection, or named entity recognition.
- Follow the tutorials, implement the techniques, and evaluate your results.
Practice Regular Expression Exercises
Show steps
Sharpen your regular expression skills, essential for text manipulation and search operations.
Browse courses on
Regular Expressions
Show steps
Show steps
Show steps
- Solve practice problems on RegExr or Regex101.
- Apply your skills to a text cleaning task, such as removing punctuation from a dataset.
Attend a Workshop on Natural Language Processing Tools
Show steps
Enhance your skills by attending a workshop that provides hands-on experience with popular NLP tools and techniques.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Research and identify relevant workshops offered by universities, industry organizations, or online platforms.
- Register for a workshop that aligns with your learning goals.
Create a Topic Modeling Visualization
Show steps
Reinforce your understanding of topic modeling by visualizing the topics identified in a text corpus.
Browse courses on
Topic Modeling
Show steps
Show steps
Show steps
- Use a library like gensim or scikit-learn to perform topic modeling on a dataset.
- Create a visualization of the topics using techniques such as word clouds or interactive plots.
Career center
Learners who complete Applied Text Mining in Python will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer can use text mining and natural language processing to develop systems that can understand and generate text. This course can help build the skills needed to work with text data, which is becoming increasingly important in many industries, such as customer service, healthcare, and finance.
Data Scientist
Data Scientist
A Data Scientist can use their skills in text mining and natural language processing to derive insights from unstructured data, such as customer reviews, social media posts, and news articles. This course can help build a foundation in these techniques, which can be applied in many different industries, such as finance, healthcare, and marketing.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer can use text mining and natural language processing to develop machine learning models that can understand and generate text. This course can help build the skills needed to work with text data, which is becoming increasingly important in machine learning.
Data Analyst
Data Analyst
A Data Analyst can use text mining and natural language processing to analyze unstructured data, such as customer reviews, social media posts, and news articles. This course can help build the skills needed to work with text data, which is becoming increasingly important in many industries.
Business Analyst
Business Analyst
A Business Analyst can use text mining and natural language processing to analyze unstructured data, such as customer reviews, social media posts, and news articles, to gain insights into customer behavior, market trends, and other business-related topics. This course can help build the skills needed to work with text data, which is becoming increasingly important in business.
Information Architect
Information Architect
An Information Architect can use text mining and natural language processing to design and organize websites and other information systems. This course can help build the skills needed to work with text data, which is becoming increasingly important in information architecture.
User Experience Designer
User Experience Designer
A User Experience Designer can use text mining and natural language processing to improve the user experience of websites and other digital products. This course can help build the skills needed to work with text data, which is becoming increasingly important in user experience design.
Content Strategist
Content Strategist
A Content Strategist can use text mining and natural language processing to develop and manage content for websites and other digital products. This course can help build the skills needed to work with text data, which is becoming increasingly important in content strategy.
Technical Writer
Technical Writer
A Technical Writer can use text mining and natural language processing to create and manage technical documentation. This course can help build the skills needed to work with text data, which is becoming increasingly important in technical writing.
Copywriter
Copywriter
A Copywriter can use text mining and natural language processing to create and manage marketing materials. This course can help build the skills needed to work with text data, which is becoming increasingly important in copywriting.
Editor
Editor
An Editor can use text mining and natural language processing to improve the quality of written content. This course can help build the skills needed to work with text data, which is becoming increasingly important in editing.
Librarian
Librarian
A Librarian can use text mining and natural language processing to organize and manage library collections. This course can help build the skills needed to work with text data, which is becoming increasingly important in librarianship.
Archivist
Archivist
An Archivist can use text mining and natural language processing to organize and manage archival collections. This course can help build the skills needed to work with text data, which is becoming increasingly important in archives.
Museum curator
Museum curator
A Museum Curator can use text mining and natural language processing to organize and manage museum collections. This course can help build the skills needed to work with text data, which is becoming increasingly important in museum curation.
Historian
Historian
A Historian can use text mining and natural language processing to analyze historical documents. This course may help build the skills needed to work with text data, which is becoming increasingly important in history.
For more career information including salaries, visit:
OpenCourser.com/course/1mfgn0/applied
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Applied Text Mining in Python.
Provides a comprehensive overview of natural language processing (NLP) techniques and algorithms, with a focus on Python implementations. It covers topics such as text classification, sentiment analysis, and topic modeling, which are all relevant to the Applied Text Mining in Python course.
Provides a comprehensive overview of natural language processing, with a focus on practical applications. It covers topics such as text classification, sentiment analysis, and topic modeling. It valuable resource for anyone who wants to use NLP for text mining projects.
Save
This online course from Coursera, taught by Andrew Ng, covers the fundamentals of machine learning for text data, including topics such as text classification, text clustering, and natural language processing. The course provides a practical understanding of how to apply machine learning techniques to text mining tasks.
Provides a comprehensive overview of Python for data analysis, including topics such as data manipulation, data visualization, and machine learning. It valuable resource for anyone who wants to use Python for text mining projects.
While this book uses R instead of Python, it provides a solid foundation in text mining concepts and techniques, including text preprocessing, feature engineering, and model evaluation. The knowledge gained from this book can be easily transferred to Python-based text mining projects.
Provides a comprehensive overview of speech and language processing, including topics such as speech recognition, natural language understanding, and machine translation. It covers both the theoretical foundations and practical applications of these technologies.
Provides a practical guide to machine learning with text data. It covers topics such as feature engineering, model selection, and evaluation. Suitable for both beginners and experienced practitioners.
Save
While this book uses R instead of Python, it provides a solid foundation in text mining concepts and techniques, including text preprocessing, feature engineering, and model evaluation. The knowledge gained from this book can be easily transferred to Python-based text mining projects.
Save
While this book uses R rather than Python, it provides a solid introduction to the concepts and techniques of text mining. It covers topics such as text preprocessing, text classification, and text clustering.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/1mfgn0/applied
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Rating
4.2
Effort
Approx. 25 hours
Level
Intermediate
Via
Coursera
Institution
University of Michigan
Instructor
V. G. Vinod Vydiswaran
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Applied Data Science with Python. It's comprised of five courses:
- Applied Text Mining in Python (viewing)
- Introduction to Data Science in Python
- Applied Social Network Analysis in Python
- Applied Machine Learning in Python
- Applied Plotting, Charting & Data Representation in Python
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