We may earn an affiliate commission when you visit our partners.
Snehan Kekre
Welcome to this project-based course on Analyzing Text Data with Yellowbrick. Tasks such as assessing document similarity, topic modelling and other text mining endeavors are predicated on the notion of "closeness" or "similarity" between documents. In this course, we define various distance metrics (e.g. Euclidean, Hamming, Cosine, Manhattan, etc) and understand their merits and shortcomings as they relate to document similarity. We will apply these metrics on documents within a specific corpus and visualize our results. By the end of this course, you will be able to confidently use visual diagnostic tools from Yellowbrick to steer your machine learning workflow, vectorize text data using TF-IDF, and cluster documents using embedding techniques and appropriate metrics.
Read more
Welcome to this project-based course on Analyzing Text Data with Yellowbrick. Tasks such as assessing document similarity, topic modelling and other text mining endeavors are predicated on the notion of "closeness" or "similarity" between documents. In this course, we define various distance metrics (e.g. Euclidean, Hamming, Cosine, Manhattan, etc) and understand their merits and shortcomings as they relate to document similarity. We will apply these metrics on documents within a specific corpus and visualize our results. By the end of this course, you will be able to confidently use visual diagnostic tools from Yellowbrick to steer your machine learning workflow, vectorize text data using TF-IDF, and cluster documents using embedding techniques and appropriate metrics.
Read more
Welcome to this project-based course on Analyzing Text Data with Yellowbrick. Tasks such as assessing document similarity, topic modelling and other text mining endeavors are predicated on the notion of "closeness" or "similarity" between documents. In this course, we define various distance metrics (e.g. Euclidean, Hamming, Cosine, Manhattan, etc) and understand their merits and shortcomings as they relate to document similarity. We will apply these metrics on documents within a specific corpus and visualize our results. By the end of this course, you will be able to confidently use visual diagnostic tools from Yellowbrick to steer your machine learning workflow, vectorize text data using TF-IDF, and cluster documents using embedding techniques and appropriate metrics.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, Yellowbrick, and scikit-learn pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/cbemvc/analyze
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until December 12
Annual Subscription Savings
Unlock programs from Google, Microsoft, and IBM and join 77% of learners who reported positive career outcomes.
Only $239!
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Syllabus
Project: Analyze Text Data with Yellowbrick
Welcome to this project-based course on Analyzing Text Data with Yellowbrick. Tasks such as assessing document similarity, topic modelling and other text mining endeavors are predicated on the notion of "closeness" or "similarity" between documents. In this course, we define various distance metrics (e.g. Euclidean, Hamming, Cosine, Manhattan, etc) and understand their merits and shortcomings as they relate to document similarity. We will apply these metrics on documents within a specific corpus and visualize our results.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
If you seek to build expertise in text analysis and mining, this project is a proper fit
Concepts are explained well, making this an effective foundation for beginners
You'll study themes and methods highly relevant to research and industry
Diverse instructional media keep the learning format lively and engaging
You'll get exposure to industry-standard tools in the field
Ensure you have a stable internet connection, as the project relies on cloud-based resources
Save this course
Save Analyze Text Data with Yellowbrick to your list so you can find it easily later:
Save
Save
Reviews summary
Practical text analysis with yellowbrick
Learners say practical course on applying Yellowbrick for text analysis with engaging assignments and helpful instructor feedback. The course provides a practical approach to applying Yellowbrick for text analysis. Learners enjoyed the assignments and appreciated the instructor's feedback.
Course includes practical applications of Yellowbrick for text analysis.
"practical approach to applying Yellowbrick for text analysis"
"practical course on applying Yellowbrick"
Assignments help learners apply the concepts.
"engaging assignments"
"assignments help learners apply the concepts"
Instructor provides helpful feedback on assignments.
"helpful instructor feedback"
"appreciated the instructor's feedback"
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 Analyze Text Data with Yellowbrick with these
activities:
Linear Algebra Refresher
Show steps
Review basic linear algebra concepts, such as vectors and matrices, to strengthen understanding of distance metrics
Browse courses on
Linear Algebra
Show steps
-
Review lecture notes or textbooks on linear algebra
-
Solve practice problems to test understanding
Natural Language Processing with Python
Show steps
Gain a foundational understanding of NLP concepts and techniques related to the course
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Read chapters relevant to text similarity and distance metrics
-
Complete practice exercises to reinforce concepts
Summarize Course Content
Show steps
Review and organize notes, assignments, and quizzes to strengthen understanding
Show steps
-
Gather and organize course materials
-
Summarize key concepts and topics
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss Distance Metrics with Peers
Show steps
Engage with peers to exchange perspectives and deepen understanding of various distance metrics
Browse courses on
Distance Metrics
Show steps
-
Find a study group or discussion forum
-
Share ideas and discuss the strengths and limitations of different distance metrics
Yellowbrick Visualization Techniques
Show steps
Follow online tutorials to learn how to use Yellowbrick's visualization tools for text data
Browse courses on
Yellowbrick
Show steps
-
Find Yellowbrick documentation or tutorials
-
Follow instructions to explore Yellowbrick's visualizations
Euclidean Distance Calculations
Show steps
Practice calculating Euclidean Distance between documents to understand their 'closeness'
Browse courses on
Euclidean Distance
Show steps
-
Find two documents from the course corpus
-
Extract and vectorize text from each document
-
Calculate Euclidean Distance between vectors
Visualize Hamming Distance Results
Show steps
Create a scatterplot or bar chart showing Hamming distances between clusters of documents
Show steps
-
Calculate Hamming Distance between documents
-
Cluster documents using Hamming Distance
-
Visualize clusters using a scatterplot or bar chart
Document Similarity Analysis
Show steps
Analyze a corpus of documents and identify groups of similar documents based on TF-IDF vectors and Cosine Similarity
Browse courses on
Document Similarity
Show steps
-
Calculate Cosine Similarity between documents
-
Collect and import a corpus of documents
-
Vectorize documents using TF-IDF
-
Cluster documents based on Cosine Similarity
Linear Algebra Refresher
Show steps
Review basic linear algebra concepts, such as vectors and matrices, to strengthen understanding of distance metrics
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review lecture notes or textbooks on linear algebra
- Solve practice problems to test understanding
Natural Language Processing with Python
Show steps
Gain a foundational understanding of NLP concepts and techniques related to the course
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Read chapters relevant to text similarity and distance metrics
- Complete practice exercises to reinforce concepts
Summarize Course Content
Show steps
Review and organize notes, assignments, and quizzes to strengthen understanding
Show steps
Show steps
Show steps
- Gather and organize course materials
- Summarize key concepts and topics
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss Distance Metrics with Peers
Show steps
Engage with peers to exchange perspectives and deepen understanding of various distance metrics
Browse courses on
Distance Metrics
Show steps
Show steps
Show steps
- Find a study group or discussion forum
- Share ideas and discuss the strengths and limitations of different distance metrics
Yellowbrick Visualization Techniques
Show steps
Follow online tutorials to learn how to use Yellowbrick's visualization tools for text data
Browse courses on
Yellowbrick
Show steps
Show steps
Show steps
- Find Yellowbrick documentation or tutorials
- Follow instructions to explore Yellowbrick's visualizations
Euclidean Distance Calculations
Show steps
Practice calculating Euclidean Distance between documents to understand their 'closeness'
Browse courses on
Euclidean Distance
Show steps
Show steps
Show steps
- Find two documents from the course corpus
- Extract and vectorize text from each document
- Calculate Euclidean Distance between vectors
Visualize Hamming Distance Results
Show steps
Create a scatterplot or bar chart showing Hamming distances between clusters of documents
Show steps
Show steps
Show steps
- Calculate Hamming Distance between documents
- Cluster documents using Hamming Distance
- Visualize clusters using a scatterplot or bar chart
Document Similarity Analysis
Show steps
Analyze a corpus of documents and identify groups of similar documents based on TF-IDF vectors and Cosine Similarity
Browse courses on
Document Similarity
Show steps
Show steps
Show steps
- Calculate Cosine Similarity between documents
- Collect and import a corpus of documents
- Vectorize documents using TF-IDF
- Cluster documents based on Cosine Similarity
Career center
Learners who complete Analyze Text Data with Yellowbrick will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts often collect, process, and interpret text data. This course can help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as text mining, natural language processing, and data visualization. These skills are essential for Data Analysts who want to work with text data and gain insights from it.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers often work with text data. This course can help you develop the skills needed to build machine learning models that can analyze text data and make predictions. The course covers topics such as natural language processing, machine learning, and data visualization. These skills are essential for Machine Learning Engineers who want to work with text data.
Data Scientist
Data Scientist
Data Scientists often work with text data. This course can help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and machine learning. These skills are essential for Data Scientists who want to work with text data and gain insights from it.
Business Analyst
Business Analyst
Business Analysts often work with text data. This course can help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization. These skills are essential for Business Analysts who want to work with text data and gain insights from it.
Information Security Analyst
Information Security Analyst
Information Security Analysts often work with text data. This course can help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization. These skills are essential for Information Security Analysts who want to work with text data and gain insights from it.
Software Engineer
Software Engineer
Software Engineers often work with text data. This course can help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization. These skills are essential for Software Engineers who want to work with text data and gain insights from it.
Web Developer
Web Developer
Web Developers often work with text data. This course can help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization. These skills are essential for Web Developers who want to work with text data and gain insights from it.
Database Administrator
Database Administrator
Database Administrators often work with text data. This course can help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization. These skills are essential for Database Administrators who want to work with text data and gain insights from it.
IT Manager
IT Manager
IT Managers often work with text data. This course can help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization. These skills are essential for IT Managers who want to work with text data and gain insights from it.
Project Manager
Project Manager
Project Managers often work with text data. This course may help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization.
Marketing Manager
Marketing Manager
Marketing Managers often work with text data. This course may help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization.
Sales Manager
Sales Manager
Sales Managers often work with text data. This course may help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization.
Customer Service Manager
Customer Service Manager
Customer Service Managers often work with text data. This course may help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization.
Human Resources Manager
Human Resources Manager
Human Resources Managers often work with text data. This course may help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization.
Financial Analyst
Financial Analyst
Financial Analysts often work with text data. This course may help you develop the skills needed to analyze text data and gain insights from it. The course covers topics such as data mining, natural language processing, and data visualization.
For more career information including salaries, visit:
OpenCourser.com/course/cbemvc/analyze
Reading list
We've selected eight 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
Analyze Text Data with Yellowbrick.
Provides a comprehensive overview of natural language processing (NLP) techniques, including text classification, clustering, and information extraction. It valuable resource for anyone interested in learning more about NLP.
Provides a comprehensive overview of machine learning techniques for big data. It covers a wide range of topics, including data preprocessing, feature engineering, and model training. It valuable resource for anyone interested in learning more about machine learning for big data.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone interested in learning more about machine learning.
Save
Provides a comprehensive overview of deep learning techniques using R. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in learning more about deep learning using R.
Provides a comprehensive overview of natural language processing using Python and NLTK. It covers a wide range of topics, including text preprocessing, feature extraction, and text classification. It valuable resource for anyone interested in learning more about natural language processing using Python and NLTK.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning. It valuable resource for anyone interested in learning more about machine learning.
Provides a comprehensive overview of deep learning techniques, including convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone interested in learning more about deep learning.
Provides a practical introduction to natural language processing using Python. It covers a wide range of topics, including text preprocessing, feature extraction, and text classification. It valuable resource for anyone interested in learning more about natural language processing.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cbemvc/analyze
Share
Similar courses
Here are nine courses similar to
Analyze Text Data with Yellowbrick.
Quantitative Text Analysis and Textual Similarity in R
OpenCourser.com/course/tl2mse/quantitative
Quantitative Text Analysis and Textual Similarity in R
Most relevant
Visual Machine Learning with Yellowbrick
OpenCourser.com/course/okecxq/visual
Visual Machine Learning with Yellowbrick
Most relevant
Regression Analysis with Yellowbrick
OpenCourser.com/course/i4z3qz/regression
Regression Analysis with Yellowbrick
Most relevant
Perform Feature Analysis with Yellowbrick
OpenCourser.com/course/pcpo1m/perform
Perform Feature Analysis with Yellowbrick
Most relevant
Machine Learning: Clustering & Retrieval
OpenCourser.com/course/a60amb/machine
Machine Learning: Clustering & Retrieval
Most relevant
Evaluate Machine Learning Models with Yellowbrick
OpenCourser.com/course/f7sqfh/evaluate
Evaluate Machine Learning Models with Yellowbrick
Most relevant
Applied Text Mining in Python
OpenCourser.com/course/1mfgn0/applied
Applied Text Mining in Python
Text Generation with Cohere: Recognizing Similarities
OpenCourser.com/course/6nxqg9/text
Text Generation with Cohere: Recognizing Similarities
TensorFlow for NLP: Semantic Similarity in Texts
OpenCourser.com/course/ydmo25/tensorflow
TensorFlow for NLP: Semantic Similarity in Texts
Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
If you seek to build expertise in text analysis and mining, this project is a proper fit
Concepts are explained well, making this an effective foundation for beginners
You'll study themes and methods highly relevant to research and industry
Diverse instructional media keep the learning format lively and engaging
You'll get exposure to industry-standard tools in the field
Ensure you have a stable internet connection, as the project relies on cloud-based resources
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser