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Bhaskarjit Sarmah
Welcome to this 1-hour project-based course on Building Similarity Based Recommendation System. In this project, you will learn how similarity based collaborative filtering recommendation systems work, how you can collect data for building such systems. You...
Read more
Welcome to this 1-hour project-based course on Building Similarity Based Recommendation System. In this project, you will learn how similarity based collaborative filtering recommendation systems work, how you can collect data for building such systems. You...
Read more
Welcome to this 1-hour project-based course on Building Similarity Based Recommendation System. In this project, you will learn how similarity based collaborative filtering recommendation systems work, how you can collect data for building such systems. You will learn what are some different ways you to compute similarity between users and recommend items based on products interacted by other similar users. You will learn to create user item interactions matrix from the original dataset and also how to recommend items to a new user who does not have any historical interactions with the items.
Note: 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/kmnwkz/building
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Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for professionals in the North America region seeking to develop proficiency
Ideal for those interested in developing collaborative filtering recommendation systems
Prior experience in user item interaction and similarity computation is recommended
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Reviews summary
Collaborative filtering course with rhyme implementation concerns
Students had mixed feelings about this course called "Building Similarity Based Recommendation System."
Of its 7 reviews, 3 were very positive (4-5 stars) while the other 4 were negative (1-3). Students who liked the course appreciated the content, especially the projects, and found it particularly valuable to build a basic recommendation system from scratch. Those who disliked the course mainly pointed to technical issues with the Rhyme implementation. Specifically, they noted that the user interface was difficult to use, with a small workspace and limitations on video playback, and that the code used was sometimes complex and difficult to follow. Some reviewers were also frustrated with the final quiz, indicating that the correct answers were not always clear and that some questions could not be answered without looking them up in the exercise materials.
Course projects were valuable.
"Very nice projects! It gave me new insights about how to solve other problems"
Code was sometimes complex.
"The author often uses single lines of advanced code which can be difficult to follow if you aren't proficient in Python."
"This can be a nuisance or a learning opportunity, so it is up to you."
Rhyme implementation had technical issues.
"OK course, but an absolutely hideous implementation."
"You must use a virtual Windows machine in Rhyme."
"Rhyme is super annoying to use, and your actual workspace ends up being about the size of a mobile phone screen."
"If I wasn't desperate, I would have quit"
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 Building Similarity Based Recommendation System with these
activities:
Explore the Course's GitHub Repository
Show steps
Familiarize yourself with the course materials and get a head start.
Show steps
-
Clone or download the GitHub repository.
-
Explore the code and examples provided.
Form a Study Group with Classmates
Show steps
Enhance your learning through peer-to-peer discussions.
Show steps
-
Identify classmates interested in collaborating.
-
Set up regular study sessions to discuss course concepts.
Review Vector Space Representation and Cosine Similarity
Show steps
Strengthen your foundation by revising concepts essential for collaborative filtering.
Browse courses on
Cosine Similarity
Show steps
-
Review the concept of vector space representation.
-
Practice computing cosine similarity between vectors.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a Workshop on Advanced Recommendation Systems
Show steps
Expand your knowledge and connect with other professionals in the field.
Browse courses on
Recommendation Systems
Show steps
-
Search for and identify relevant workshops.
-
Register and actively participate in the workshop.
Implement collaborative filtering using similarity matrix
Show steps
Solidify your theoretical understanding of collaborative filtering by coding it yourself.
Browse courses on
Collaborative Filtering
Show steps
-
Import necessary libraries like Pandas, NumPy, and Scikit-Learn.
-
Load the dataset into a DataFrame and create a user-item matrix.
-
Compute the cosine similarity between users.
-
Recommend items to a new user based on similar users' interactions.
Develop a Whitepaper on Collaborative Filtering Best Practices
Show steps
Deepen your understanding by researching and synthesizing expert insights.
Browse courses on
Collaborative Filtering
Show steps
-
Research and gather information on collaborative filtering best practices.
-
Organize and write a comprehensive whitepaper.
Build a Movie Recommendation App
Show steps
Reinforce your understanding of collaborative filtering by creating a practical application.
Browse courses on
Movie Recommendation
Show steps
-
Design the UI/UX for your app.
-
Integrate the collaborative filtering algorithm into your app.
-
Deploy your app and share it with others.
Develop a Recommendation Engine for E-commerce
Show steps
Challenge yourself to apply collaborative filtering in a real-world scenario.
Show steps
-
Gather and preprocess product and user data.
-
Implement collaborative filtering to generate product recommendations.
-
Evaluate and improve your recommendation engine's performance.
Explore the Course's GitHub Repository
Show steps
Familiarize yourself with the course materials and get a head start.
Show steps
Show steps
Show steps
- Clone or download the GitHub repository.
- Explore the code and examples provided.
Form a Study Group with Classmates
Show steps
Enhance your learning through peer-to-peer discussions.
Show steps
Show steps
Show steps
- Identify classmates interested in collaborating.
- Set up regular study sessions to discuss course concepts.
Review Vector Space Representation and Cosine Similarity
Show steps
Strengthen your foundation by revising concepts essential for collaborative filtering.
Browse courses on
Cosine Similarity
Show steps
Show steps
Show steps
- Review the concept of vector space representation.
- Practice computing cosine similarity between vectors.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend a Workshop on Advanced Recommendation Systems
Show steps
Expand your knowledge and connect with other professionals in the field.
Browse courses on
Recommendation Systems
Show steps
Show steps
Show steps
- Search for and identify relevant workshops.
- Register and actively participate in the workshop.
Implement collaborative filtering using similarity matrix
Show steps
Solidify your theoretical understanding of collaborative filtering by coding it yourself.
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Import necessary libraries like Pandas, NumPy, and Scikit-Learn.
- Load the dataset into a DataFrame and create a user-item matrix.
- Compute the cosine similarity between users.
- Recommend items to a new user based on similar users' interactions.
Develop a Whitepaper on Collaborative Filtering Best Practices
Show steps
Deepen your understanding by researching and synthesizing expert insights.
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Research and gather information on collaborative filtering best practices.
- Organize and write a comprehensive whitepaper.
Build a Movie Recommendation App
Show steps
Reinforce your understanding of collaborative filtering by creating a practical application.
Browse courses on
Movie Recommendation
Show steps
Show steps
Show steps
- Design the UI/UX for your app.
- Integrate the collaborative filtering algorithm into your app.
- Deploy your app and share it with others.
Develop a Recommendation Engine for E-commerce
Show steps
Challenge yourself to apply collaborative filtering in a real-world scenario.
Show steps
Show steps
Show steps
- Gather and preprocess product and user data.
- Implement collaborative filtering to generate product recommendations.
- Evaluate and improve your recommendation engine's performance.
Career center
Learners who complete Building Similarity Based Recommendation System will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
A Data Engineer is responsible for building and maintaining data pipelines. They collect, transform, and analyze data to provide insights to businesses. This course can help build a foundation for a Data Engineer by teaching them about data collection, data processing, and data analysis.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models. They use data to train models that can make predictions or decisions. This course can help build a foundation for a Machine Learning Engineer by teaching them about data collection, data processing, and machine learning.
Data Scientist
Data Scientist
A Data Scientist uses data to solve business problems. They collect, analyze, and interpret data to provide insights to businesses. This course can help build a foundation for a Data Scientist by teaching them about data collection, data analysis, and data visualization.
Product Manager
Product Manager
A Product Manager is responsible for developing and managing products. They use data to understand customers and improve products. This course can help build a foundation for a Product Manager by teaching them about data collection, data analysis, and data visualization.
Business Analyst
Business Analyst
A Business Analyst uses data to help businesses make better decisions. They collect, analyze, and interpret data to provide insights to businesses. This course can help build a foundation for a Business Analyst by teaching them about data collection, data analysis, and data visualization.
Marketing Analyst
Marketing Analyst
A Marketing Analyst uses data to understand customers and improve marketing campaigns. They collect, analyze, and interpret data to provide insights to businesses. This course can help build a foundation for a Marketing Analyst by teaching them about data collection, data analysis, and data visualization.
Actuary
Actuary
An Actuary uses data to assess risk. They collect, analyze, and interpret data to provide insights to businesses and organizations. This course can help build a foundation for an Actuary by teaching them about data collection, data analysis, and data visualization.
Economist
Economist
An Economist uses data to study the economy. They collect, analyze, and interpret data to provide insights to businesses and organizations. This course can help build a foundation for an Economist by teaching them about data collection, data analysis, and data visualization.
Management Consultant
Management Consultant
A Management Consultant uses data to help businesses make better decisions. They collect, analyze, and interpret data to provide insights to businesses. This course can help build a foundation for a Management Consultant by teaching them about data collection, data analysis, and data visualization.
Researcher
Researcher
A Researcher uses data to conduct research. They collect, analyze, and interpret data to provide insights to businesses and organizations. This course can help build a foundation for a Researcher by teaching them about data collection, data analysis, and data visualization.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to improve business operations. They collect, analyze, and interpret data to provide insights to businesses. This course can help build a foundation for an Operations Research Analyst by teaching them about data collection, data analysis, and data visualization.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. They use data to provide insights to businesses and organizations. This course can help build a foundation for a Statistician by teaching them about data collection, data analysis, and data visualization.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software. They use data to improve software quality and performance. This course can help build a foundation for a Software Engineer by teaching them about data collection, data analysis, and data visualization.
Data Analyst
Data Analyst
A Data Analyst collects, analyzes, and interprets data. They use data to provide insights to businesses. This course can help build a foundation for a Data Analyst by teaching them about data collection, data analysis, and data visualization.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses data to make investment decisions. They collect, analyze, and interpret data to provide insights to businesses. This course can help build a foundation for a Quantitative Analyst by teaching them about data collection, data analysis, and data visualization.
For more career information including salaries, visit:
OpenCourser.com/course/kmnwkz/building
Reading list
We've selected six 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
Building Similarity Based Recommendation System.
This handbook provides a comprehensive overview of recommender systems, covering both theoretical and practical aspects. It valuable resource for researchers and practitioners alike.
Save
Provides a practical guide to building recommender systems.
Provides a comprehensive overview of collaborative filtering for recommender systems.
This textbook provides a comprehensive overview of recommender systems, covering both theoretical and practical aspects. It valuable resource for researchers and practitioners alike.
Save
Provides a comprehensive overview of data mining, covering the foundations, algorithms, and applications of data mining.
Provides a comprehensive overview of machine learning techniques used in recommender systems. It covers a wide range of topics, from basic concepts to advanced algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kmnwkz/building
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Bhaskarjit Sarmah
Good to know
Suitable for professionals in the North America region seeking to develop proficiency
Ideal for those interested in developing collaborative filtering recommendation systems
Prior experience in user item interaction and similarity computation is recommended
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