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Jaekwang KIM
In this course you will:
a) understand the basic concept of recommender systems. b) understand the Collaborative Filtering.
c) understand the Recommender System with Deep Learning. d) understand the Further Issues of Recommender Systems.
Read more
In this course you will:
a) understand the basic concept of recommender systems. b) understand the Collaborative Filtering.
c) understand the Recommender System with Deep Learning. d) understand the Further Issues of Recommender Systems.
Read more
In this course you will:
a) understand the basic concept of recommender systems. b) understand the Collaborative Filtering.
c) understand the Recommender System with Deep Learning. d) understand the Further Issues of Recommender Systems.
Please make sure that you’re comfortable programming in Python and have a basic knowledge of mathematics including matrix multiplications, conditional probability, and basic machine learning algorithms.
Register for this course and see more details by visiting:
OpenCourser.com/course/6giyue/recommender
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What's inside
Syllabus
Introduction to Recommender Systems
Collaborative Filtering
Recommender System with Deep Learning
Read more
Syllabus
Introduction to Recommender Systems
Collaborative Filtering
Recommender System with Deep Learning
Read more
Further Understanding of Recommender Systems
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Useful for people with some programming experience and knowledge of machine learning algorithms
Covers important topics in recommender systems, including collaborative filtering and deep learning
Led by instructors who are experts in the field
Provides hands-on experience through labs and interactive materials
Students are expected to have a basic understanding of mathematics, including matrix multiplications, conditional probability, and basic machine learning algorithms
The course does not cover the latest advancements in recommender systems research
Save this course
Save Recommender Systems to your list so you can find it easily later:
Save
Save
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 Recommender Systems with these
activities:
Review linear algebra
Show steps
Review the basics of linear algebra to strengthen your foundation for this course.
Browse courses on
Linear Algebra
Show steps
-
Revisit matrix operations, such as addition, subtraction, and multiplication.
-
Practice solving systems of linear equations using techniques like Gaussian elimination.
-
Review concepts of vector spaces, including vector addition and scalar multiplication.
Explore Python libraries for recommender systems
Show steps
Enhance your understanding of building recommender systems by following guided tutorials that demonstrate the use of popular Python libraries.
Browse courses on
Recommender Systems
Show steps
-
Install and set up the necessary Python libraries like Surprise or LightFM.
-
Work through tutorials to explore data loading, model training, and evaluation techniques.
-
Experiment with different library features and compare their performance.
Practice collaborative filtering algorithms
Show steps
Strengthen your understanding of collaborative filtering algorithms by solving practice problems and implementing them from scratch.
Browse courses on
Collaborative Filtering
Show steps
-
Implement a user-based collaborative filtering algorithm using cosine similarity or Pearson correlation.
-
Build an item-based collaborative filtering algorithm using TF-IDF or Jaccard similarity.
-
Explore advanced techniques like matrix factorization and singular value decomposition.
Three other activities
Expand to see all activities and additional details
Show all six activities
Design a recommender system for a specific domain
Show steps
Apply your knowledge by designing and implementing a recommender system for a specific domain, such as movies, products, or music.
Show steps
-
Define the problem statement and gather relevant data.
-
Choose suitable collaborative filtering or deep learning techniques.
-
Develop and train the recommender system model.
-
Evaluate the performance of the system using relevant metrics.
Participate in a recommender systems hackathon
Show steps
Challenge yourself and test your skills by participating in a recommender systems hackathon, where you can collaborate and compete with others.
Browse courses on
Kaggle Competitions
Show steps
-
Identify relevant hackathons or competitions.
-
Form a team or work individually on a solution.
-
Develop and implement a recommender system.
-
Submit your solution and compete for prizes or recognition.
Contribute to open-source recommender systems projects
Show steps
Gain practical experience and contribute to the community by participating in open-source recommender systems projects.
Show steps
-
Identify open-source recommender systems projects that align with your interests.
-
Review the project documentation and codebase.
-
Contribute code, documentation, or bug fixes.
Review linear algebra
Show steps
Review the basics of linear algebra to strengthen your foundation for this course.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Revisit matrix operations, such as addition, subtraction, and multiplication.
- Practice solving systems of linear equations using techniques like Gaussian elimination.
- Review concepts of vector spaces, including vector addition and scalar multiplication.
Explore Python libraries for recommender systems
Show steps
Enhance your understanding of building recommender systems by following guided tutorials that demonstrate the use of popular Python libraries.
Browse courses on
Recommender Systems
Show steps
Show steps
Show steps
- Install and set up the necessary Python libraries like Surprise or LightFM.
- Work through tutorials to explore data loading, model training, and evaluation techniques.
- Experiment with different library features and compare their performance.
Practice collaborative filtering algorithms
Show steps
Strengthen your understanding of collaborative filtering algorithms by solving practice problems and implementing them from scratch.
Browse courses on
Collaborative Filtering
Show steps
Show steps
Show steps
- Implement a user-based collaborative filtering algorithm using cosine similarity or Pearson correlation.
- Build an item-based collaborative filtering algorithm using TF-IDF or Jaccard similarity.
- Explore advanced techniques like matrix factorization and singular value decomposition.
Three other activities
Expand to see all activities and additional details
Show all six activities
Design a recommender system for a specific domain
Show steps
Apply your knowledge by designing and implementing a recommender system for a specific domain, such as movies, products, or music.
Show steps
Show steps
Show steps
- Define the problem statement and gather relevant data.
- Choose suitable collaborative filtering or deep learning techniques.
- Develop and train the recommender system model.
- Evaluate the performance of the system using relevant metrics.
Participate in a recommender systems hackathon
Show steps
Challenge yourself and test your skills by participating in a recommender systems hackathon, where you can collaborate and compete with others.
Browse courses on
Kaggle Competitions
Show steps
Show steps
Show steps
- Identify relevant hackathons or competitions.
- Form a team or work individually on a solution.
- Develop and implement a recommender system.
- Submit your solution and compete for prizes or recognition.
Contribute to open-source recommender systems projects
Show steps
Gain practical experience and contribute to the community by participating in open-source recommender systems projects.
Show steps
Show steps
Show steps
- Identify open-source recommender systems projects that align with your interests.
- Review the project documentation and codebase.
- Contribute code, documentation, or bug fixes.
Career center
Learners who complete Recommender Systems will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. They use statistical methods and machine learning techniques to build predictive models. This course may be useful for Data Analysts because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Data Scientist
Data Scientist
Data Scientists study data to extract meaningful insights. They use statistical methods and machine learning techniques to build predictive models. This course may be useful for Data Scientists because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. They work with data scientists to determine the best models for a given problem and then develop and deploy those models. This course may be useful for Machine Learning Engineers because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Recruiter
Recruiter
Recruiters find and hire qualified candidates to fill open positions within an organization. They work with a variety of stakeholders to identify and attract top talent. This course may be useful for Recruiters because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Business Analyst
Business Analyst
Business Analysts help organizations to improve their performance by identifying and solving problems. They use a variety of techniques, including data analysis, to understand the needs of an organization and develop solutions to those needs. This course may be useful for Business Analysts because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for the management of human resources within an organization. They work with a variety of stakeholders to develop and implement HR policies and procedures. This course may be useful for Human Resources Managers because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Financial Analyst
Financial Analyst
Financial Analysts provide analysis and advice on financial matters to individuals and organizations. They work with a variety of data to identify trends and patterns and make recommendations on investment decisions. This course may be useful for Financial Analysts because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to create products that meet the needs of users. This course may be useful for Product Managers because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of an organization. They work with a variety of teams to ensure that the organization runs smoothly and efficiently. This course may be useful for Operations Managers because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Consultant
Consultant
Consultants provide advice and guidance to organizations on a variety of topics. They work with clients to identify problems, develop solutions, and implement those solutions. This course may be useful for Consultants because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for the happiness and satisfaction of customers. They work with customers to resolve problems, provide support, and identify opportunities for growth. This course may be useful for Customer Success Managers because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course may be useful for Software Engineers because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the planning and execution of marketing campaigns. They work with a variety of teams to create marketing materials, manage social media, and track campaign results. This course may be useful for Marketing Managers because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Project Manager
Project Manager
Project Managers are responsible for the planning, execution, and completion of projects. They work with a variety of stakeholders to ensure that projects are completed on time, within budget, and to the required quality. This course may be useful for Project Managers because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
Sales Manager
Sales Manager
Sales Managers are responsible for the sales performance of a team of salespeople. They work with salespeople to develop sales strategies, track sales results, and provide training. This course may be useful for Sales Managers because it introduces the basic concepts of recommender systems, collaborative filtering, and recommender systems with deep learning.
For more career information including salaries, visit:
OpenCourser.com/course/6giyue/recommender
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
Recommender Systems.
While not specifically focused on recommender systems, this book provides a strong foundation in deep learning techniques commonly used in natural language processing, which can be applied to many types of recommender systems.
Provides a theoretical foundation for recommender systems. It covers a wide range of topics, including collaborative filtering, matrix factorization, and deep learning.
Provides a comprehensive overview of information retrieval techniques, including search engines and recommender systems, offering a broader understanding of the underlying principles and algorithms.
A comprehensive textbook covering the key concepts and techniques in recommender systems, offering a comprehensive and structured learning experience.
A practical guide to applying machine learning techniques in real-world scenarios, offering insights into the challenges and best practices of implementing recommender systems.
Save
Provides an overview of advanced topics in recommender systems. It covers a wide range of topics, including collaborative filtering, matrix factorization, and deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/6giyue/recommender
Share
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Effort
4 weeks of study, 2 hours/week
Level
Intermediate
Via
Coursera
Institution
Sungkyunkwan University
Instructor
Jaekwang KIM
Language
English
Good to know
Useful for people with some programming experience and knowledge of machine learning algorithms
Covers important topics in recommender systems, including collaborative filtering and deep learning
Led by instructors who are experts in the field
Provides hands-on experience through labs and interactive materials
Students are expected to have a basic understanding of mathematics, including matrix multiplications, conditional probability, and basic machine learning algorithms
The course does not cover the latest advancements in recommender systems research
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