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Music Recommender System Using Pyspark
Ahmad Varasteh
Ahmad Varasteh
Nowadays, recommender systems are everywhere. for example, Amazon uses recommender systems to suggest some products that you might be interested in based on the products you've bought earlier. Or Spotify will suggest new tracks based on the songs you use to...
Read more
Nowadays, recommender systems are everywhere. for example, Amazon uses recommender systems to suggest some products that you might be interested in based on the products you've bought earlier. Or Spotify will suggest new tracks based on the songs you use to...
Read more
Nowadays, recommender systems are everywhere. for example, Amazon uses recommender systems to suggest some products that you might be interested in based on the products you've bought earlier. Or Spotify will suggest new tracks based on the songs you use to listen to every day. Most of these recommender systems use some algorithms which are based on Matrix factorization such as NMF( NON NEGATIVE MATRIX FACTORIZATION) or ALS (Alternating Least Square).
So in this Project, we are going to use ALS Algorithm to create a Music Recommender system to suggest new tracks to different users based upon the songs they've been listening to. As a very important prerequisite of this course, I suggest you study a little bit about ALS Algorithm because in this course we will not cover any theoretical concepts.
Note: This project 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/4asxvh/music
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Assumes a prerequisite knowledge of the Alternating Least Square (ALS) Algorithm
Appropriate for those interested in building a music recommender system using the ALS Algorithm
Focuses on the practical application of the ALS Algorithm in a specific domain
A foundational understanding of linear algebra and matrix operations would be beneficial
Suitable for learners based in North America, as the course is tailored to that region
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Reviews summary
Music rec system with pyspark
This course on building a music recommender system using Pyspark receives mixed reviews, with some students appreciating its ease of learning and others finding it incomplete or lacking in substance. It's important to note that the course assumes prior knowledge of the ALS algorithm and may be most suitable for learners in North America.
Easy-to-follow guided projects
"easy to learn these guided projects"
Assumes prior knowledge of ALS
"As a very important prerequisite of this course, I suggest you study a little bit about ALS Algorithm because in this course we will not cover any theoretical concepts."
Course lacks substance and may be incomplete
"...impossible to complete as the dataset is not available..."
"...you will find the same thing just by looking on medium articles..."
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 Music Recommender System Using Pyspark with these
activities:
Review matrix factorization techniques
Show steps
Review core concepts in matrix factorization to solidify your mathematical foundation and prepare yourself for the upcoming course materials.
Browse courses on
Matrix Factorization
Show steps
-
Read online tutorials or articles about matrix factorization.
-
Solve practice problems or exercises related to matrix factorization.
Connect with Experts
Show steps
Reach out to experts in the field of recommender systems or machine learning to gain insights and guidance on your learning journey.
Show steps
-
Identify potential mentors through online platforms or social media
-
Send a personalized message introducing yourself and expressing interest
Assist Peers in Understanding ALS and Music Recommendation
Show steps
Share your knowledge and assist fellow learners in understanding the concepts and techniques covered in the course.
Show steps
-
Identify opportunities to provide support in online forums or study groups.
-
Provide clear and concise explanations of concepts.
-
Share your own experiences and insights.
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Organize and review course materials
Show steps
Stay organized and ensure you have a solid understanding of the course materials by reviewing and compiling them.
Show steps
-
Go through the course syllabus and identify key concepts and topics.
-
Organize your notes, assignments, quizzes, and exams.
-
Review the materials regularly to refresh your memory and reinforce your understanding.
Review Linear Algebra
Show steps
Review the basics of linear algebra, including matrices and matrix operations, to strengthen your foundation for this course.
Browse courses on
Linear Algebra
Show steps
-
Review your lecture notes or textbooks on linear algebra
-
Solve practice problems on linear algebra topics
Organize and Review Resources
Show steps
Review the ALS algorithm, music data, and other course materials to strengthen your understanding.
Browse courses on
Recommender Systems
Show steps
-
Gather all course materials, including lecture notes, articles, and data sets.
-
Organize materials into a logical structure using folders, note-taking apps, or other organizational tools.
-
Review materials regularly to reinforce concepts and improve retention.
Compile a list of resources on music recommender systems
Show steps
Curate a collection of valuable resources to support your learning and stay up-to-date with the field.
Show steps
-
Search for articles, tutorials, and online courses on music recommender systems.
-
Organize the resources in a structured and accessible format.
-
Share the compilation with your peers or contribute it to an online community.
Explore ALS implementations
Show steps
Seek out and follow tutorials on ALS implementations to gain a deeper understanding of the algorithm.
Show steps
-
Find online tutorials for ALS implementations
-
Follow a tutorial and implement ALS in a programming language
-
Experiment with different ALS parameters and datasets
Practice implementing ALS algorithm
Show steps
Reinforce your understanding of the ALS algorithm by working through practice problems and implementing it yourself.
Show steps
-
Find online resources or tutorials that provide practice problems or coding challenges.
-
Implement the ALS algorithm in a programming language of your choice.
Practice Matrix Factorization
Show steps
Practice matrix factorization drills to reinforce your understanding of this fundamental concept.
Browse courses on
Matrix Factorization
Show steps
-
Solve practice problems on matrix factorization
-
Create your own matrix factorization algorithm in a programming language
Watch tutorials on advanced recommender system techniques
Show steps
Expand your knowledge of recommender systems by exploring more advanced techniques and algorithms.
Show steps
-
Find online courses or tutorials that cover advanced topics in recommender systems.
-
Watch the tutorials and take notes on the concepts and techniques discussed.
Contribute to Open Source
Show steps
Contribute to a recombinant system tool or framework to stay up-to-date on real-world applications of these techniques.
Browse courses on
Open Source
Show steps
-
Find an open source recommender system project
-
Review the documentation and codebase
-
Make a bug fix or feature contribution
Explore Advanced Matrix Factorization Techniques
Show steps
Enhance your understanding of matrix factorization by seeking out and completing tutorials on advanced techniques.
Browse courses on
Matrix Factorization
Show steps
-
Identify reputable sources for tutorials on advanced matrix factorization techniques.
-
Follow tutorials and implement the techniques in a coding environment.
-
Document your findings and explore applications of the techniques in the context of music recommendation.
Submit an ALS Implementation
Show steps
Submit an ALS implementation in your preferred programming language to demonstrate your understanding of the algorithm and its applications.
Show steps
-
Choose a programming language and IDE
-
Implement the ALS algorithm from scratch
-
Submit your implementation for review and feedback
Build a simple music recommender system using ALS
Show steps
Apply your knowledge of ALS and recommender systems by building a functional music recommender system from scratch.
Show steps
-
Gather a dataset of music tracks and user listening data.
-
Preprocess the data and convert it into a suitable format for matrix factorization.
-
Implement the ALS algorithm to factorize the user-item interaction matrix.
-
Use the factorized matrices to generate personalized music recommendations.
-
Evaluate the performance of your recommender system using appropriate metrics.
Build a Music Recommender System
Show steps
Create your own music recommender system using the ALS algorithm to solidify your understanding of the concepts.
Show steps
-
Gather a dataset of music tracks and user ratings
-
Implement the ALS algorithm in a programming language
-
Train your recommender system on the dataset
-
Evaluate the performance of your recommender system
Develop a Music Recommender App Prototype
Show steps
Apply your knowledge of ALS and music recommendation systems to create a functioning prototype of a music recommender app.
Browse courses on
App Development
Show steps
-
Design the user interface and functionality of the app.
-
Implement the ALS algorithm and integrate it with the app.
-
Create a dataset of music and user preferences.
-
Test and refine the app's performance using the dataset.
-
Present your prototype and findings to peers or mentors for feedback.
Contribute to an open-source music recommender project
Show steps
Gain practical experience and contribute to the community by participating in an open-source music recommender project.
Show steps
-
Find an open-source music recommender project that aligns with your interests.
-
Review the project's documentation and codebase.
-
Identify areas where you can contribute based on your skills and knowledge.
-
Make meaningful contributions to the project, such as fixing bugs or implementing new features.
Volunteer for a Music Project
Show steps
Volunteer your time to a music-related organization or project to gain hands-on experience and apply your knowledge in a practical setting.
Browse courses on
Music
Show steps
-
Identify volunteer opportunities at local music venues, clubs, or festivals
-
Apply and secure a volunteer position
-
Contribute to the project by assisting with music selection, event planning, or audience engagement
Review matrix factorization techniques
Show steps
Review core concepts in matrix factorization to solidify your mathematical foundation and prepare yourself for the upcoming course materials.
Browse courses on
Matrix Factorization
Show steps
Show steps
Show steps
- Read online tutorials or articles about matrix factorization.
- Solve practice problems or exercises related to matrix factorization.
Connect with Experts
Show steps
Reach out to experts in the field of recommender systems or machine learning to gain insights and guidance on your learning journey.
Show steps
Show steps
Show steps
- Identify potential mentors through online platforms or social media
- Send a personalized message introducing yourself and expressing interest
Assist Peers in Understanding ALS and Music Recommendation
Show steps
Share your knowledge and assist fellow learners in understanding the concepts and techniques covered in the course.
Show steps
Show steps
Show steps
- Identify opportunities to provide support in online forums or study groups.
- Provide clear and concise explanations of concepts.
- Share your own experiences and insights.
16 other activities
Expand to see all activities and additional details
Show all 19 activities
Organize and review course materials
Show steps
Stay organized and ensure you have a solid understanding of the course materials by reviewing and compiling them.
Show steps
Show steps
Show steps
- Go through the course syllabus and identify key concepts and topics.
- Organize your notes, assignments, quizzes, and exams.
- Review the materials regularly to refresh your memory and reinforce your understanding.
Review Linear Algebra
Show steps
Review the basics of linear algebra, including matrices and matrix operations, to strengthen your foundation for this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review your lecture notes or textbooks on linear algebra
- Solve practice problems on linear algebra topics
Organize and Review Resources
Show steps
Review the ALS algorithm, music data, and other course materials to strengthen your understanding.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Gather all course materials, including lecture notes, articles, and data sets.
- Organize materials into a logical structure using folders, note-taking apps, or other organizational tools.
- Review materials regularly to reinforce concepts and improve retention.
Compile a list of resources on music recommender systems
Show steps
Curate a collection of valuable resources to support your learning and stay up-to-date with the field.
Show steps
Show steps
Show steps
- Search for articles, tutorials, and online courses on music recommender systems.
- Organize the resources in a structured and accessible format.
- Share the compilation with your peers or contribute it to an online community.
Explore ALS implementations
Show steps
Seek out and follow tutorials on ALS implementations to gain a deeper understanding of the algorithm.
Show steps
Show steps
Show steps
- Find online tutorials for ALS implementations
- Follow a tutorial and implement ALS in a programming language
- Experiment with different ALS parameters and datasets
Practice implementing ALS algorithm
Show steps
Reinforce your understanding of the ALS algorithm by working through practice problems and implementing it yourself.
Show steps
Show steps
Show steps
- Find online resources or tutorials that provide practice problems or coding challenges.
- Implement the ALS algorithm in a programming language of your choice.
Practice Matrix Factorization
Show steps
Practice matrix factorization drills to reinforce your understanding of this fundamental concept.
Browse courses on
Matrix Factorization
Show steps
Show steps
Show steps
- Solve practice problems on matrix factorization
- Create your own matrix factorization algorithm in a programming language
Watch tutorials on advanced recommender system techniques
Show steps
Expand your knowledge of recommender systems by exploring more advanced techniques and algorithms.
Show steps
Show steps
Show steps
- Find online courses or tutorials that cover advanced topics in recommender systems.
- Watch the tutorials and take notes on the concepts and techniques discussed.
Contribute to Open Source
Show steps
Contribute to a recombinant system tool or framework to stay up-to-date on real-world applications of these techniques.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find an open source recommender system project
- Review the documentation and codebase
- Make a bug fix or feature contribution
Explore Advanced Matrix Factorization Techniques
Show steps
Enhance your understanding of matrix factorization by seeking out and completing tutorials on advanced techniques.
Browse courses on
Matrix Factorization
Show steps
Show steps
Show steps
- Identify reputable sources for tutorials on advanced matrix factorization techniques.
- Follow tutorials and implement the techniques in a coding environment.
- Document your findings and explore applications of the techniques in the context of music recommendation.
Submit an ALS Implementation
Show steps
Submit an ALS implementation in your preferred programming language to demonstrate your understanding of the algorithm and its applications.
Show steps
Show steps
Show steps
- Choose a programming language and IDE
- Implement the ALS algorithm from scratch
- Submit your implementation for review and feedback
Build a simple music recommender system using ALS
Show steps
Apply your knowledge of ALS and recommender systems by building a functional music recommender system from scratch.
Show steps
Show steps
Show steps
- Gather a dataset of music tracks and user listening data.
- Preprocess the data and convert it into a suitable format for matrix factorization.
- Implement the ALS algorithm to factorize the user-item interaction matrix.
- Use the factorized matrices to generate personalized music recommendations.
- Evaluate the performance of your recommender system using appropriate metrics.
Build a Music Recommender System
Show steps
Create your own music recommender system using the ALS algorithm to solidify your understanding of the concepts.
Show steps
Show steps
Show steps
- Gather a dataset of music tracks and user ratings
- Implement the ALS algorithm in a programming language
- Train your recommender system on the dataset
- Evaluate the performance of your recommender system
Develop a Music Recommender App Prototype
Show steps
Apply your knowledge of ALS and music recommendation systems to create a functioning prototype of a music recommender app.
Browse courses on
App Development
Show steps
Show steps
Show steps
- Design the user interface and functionality of the app.
- Implement the ALS algorithm and integrate it with the app.
- Create a dataset of music and user preferences.
- Test and refine the app's performance using the dataset.
- Present your prototype and findings to peers or mentors for feedback.
Contribute to an open-source music recommender project
Show steps
Gain practical experience and contribute to the community by participating in an open-source music recommender project.
Show steps
Show steps
Show steps
- Find an open-source music recommender project that aligns with your interests.
- Review the project's documentation and codebase.
- Identify areas where you can contribute based on your skills and knowledge.
- Make meaningful contributions to the project, such as fixing bugs or implementing new features.
Volunteer for a Music Project
Show steps
Volunteer your time to a music-related organization or project to gain hands-on experience and apply your knowledge in a practical setting.
Browse courses on
Music
Show steps
Show steps
Show steps
- Identify volunteer opportunities at local music venues, clubs, or festivals
- Apply and secure a volunteer position
- Contribute to the project by assisting with music selection, event planning, or audience engagement
Career center
Learners who complete Music Recommender System Using Pyspark will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their knowledge of mathematics, statistics, and programming to solve complex problems using data. They are often responsible for developing and implementing algorithms to analyze data and make predictions. The Music Recommender System Using Pyspark course can be helpful for Data Scientists by providing them with a foundation in matrix factorization, which is a commonly used technique for recommender systems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. They often work on teams to create new features and fix bugs. The Music Recommender System Using Pyspark course can be helpful for Software Engineers by providing them with experience in using Apache Spark, which is a popular big data processing framework.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and implement machine learning models. They often work with Data Scientists to identify and solve problems that can be solved using machine learning. The Music Recommender System Using Pyspark course can be helpful for Machine Learning Engineers by providing them with experience in using Apache Spark to develop and deploy machine learning models.
Data Analyst
Data Analyst
Data Analysts use their skills in statistics and programming to analyze data and make recommendations. They often work with businesses to help them make better decisions. The Music Recommender System Using Pyspark course can be useful for Data Analysts by providing them with experience in using Apache Spark to analyze large datasets.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to help businesses improve their operations. They often work with stakeholders to identify and solve problems. The Music Recommender System Using Pyspark course can be useful for Business Analysts by providing them with experience in using Apache Spark to analyze data and make recommendations.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. The Music Recommender System Using Pyspark course can be useful for Product Managers by providing them with experience in using Apache Spark to analyze data and make recommendations.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work with Data Scientists and Machine Learning Engineers to ensure that data is available for analysis and modeling. The Music Recommender System Using Pyspark course can be useful for Data Engineers by providing them with experience in using Apache Spark to process and transform data.
Web Developer
Web Developer
Web Developers design, develop, and maintain websites. They often work with designers and content writers to create websites that are both visually appealing and functional. The Music Recommender System Using Pyspark course may be useful for Web Developers by providing them with experience in using Apache Spark to process and analyze data.
Mobile Developer
Mobile Developer
Mobile Developers design, develop, and maintain mobile apps. They often work with designers and product managers to create apps that are both useful and engaging. The Music Recommender System Using Pyspark course may be useful for Mobile Developers by providing them with experience in using Apache Spark to process and analyze data.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. They work with developers and users to ensure that data is stored and accessed securely and efficiently. The Music Recommender System Using Pyspark course may be useful for Database Administrators by providing them with experience in using Apache Spark to process and analyze data.
Network Administrator
Network Administrator
Network Administrators design, implement, and maintain computer networks. They work with users and other IT professionals to ensure that networks are running smoothly and securely. The Music Recommender System Using Pyspark course may be useful for Network Administrators by providing them with experience in using Apache Spark to process and analyze data.
System Administrator
System Administrator
System Administrators design, implement, and maintain computer systems. They work with users and other IT professionals to ensure that systems are running smoothly and securely. The Music Recommender System Using Pyspark course may be useful for System Administrators by providing them with experience in using Apache Spark to process and analyze data.
Security Analyst
Security Analyst
Security Analysts design, implement, and maintain security systems. They work with users and other IT professionals to ensure that systems are protected from unauthorized access and attack. The Music Recommender System Using Pyspark course may be useful for Security Analysts by providing them with experience in using Apache Spark to process and analyze data.
Quality Assurance Engineer
Quality Assurance Engineer
Quality Assurance Engineers test and evaluate software products to ensure that they meet quality standards. They work with developers and testers to identify and fix bugs. The Music Recommender System Using Pyspark course may be useful for Quality Assurance Engineers by providing them with experience in using Apache Spark to process and analyze data.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for software products. They work with developers and other IT professionals to ensure that documentation is accurate and easy to understand. The Music Recommender System Using Pyspark course may be useful for Technical Writers by providing them with experience in using Apache Spark to process and analyze data.
For more career information including salaries, visit:
OpenCourser.com/course/4asxvh/music
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
Music Recommender System Using Pyspark.
Provides a comprehensive overview of recommender systems from a textbook perspective. It covers a wide range of topics, including user modeling, item modeling, recommendation algorithms, and evaluation methods.
Provides a comprehensive overview of Python for data analysis. It valuable resource for anyone interested in learning more about this topic.
Save
Provides a comprehensive overview of data mining. It valuable resource for anyone interested in learning more about this topic.
Provides a comprehensive overview of deep learning for coders. It valuable resource for anyone interested in learning more about this topic.
Provides a comprehensive overview of machine learning with Scikit-Learn, Keras, and TensorFlow. It valuable resource for anyone interested in learning more about this topic.
Provides a comprehensive overview of probability and statistics. It valuable resource for anyone interested in learning more about this topic.
Provides a comprehensive overview of linear algebra. It valuable resource for anyone interested in learning more about this topic.
Provides a comprehensive overview of machine learning for beginners. It valuable resource for anyone interested in learning more about this topic.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4asxvh/music
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Time
1 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ahmad Varasteh
Good to know
Assumes a prerequisite knowledge of the Alternating Least Square (ALS) Algorithm
Appropriate for those interested in building a music recommender system using the ALS Algorithm
Focuses on the practical application of the ALS Algorithm in a specific domain
A foundational understanding of linear algebra and matrix operations would be beneficial
Suitable for learners based in North America, as the course is tailored to that region
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