Recommender Systems
Master the fundamentals of recommender systems, from collaborative filtering to deep learning approaches for personalized recommendations.
Master the fundamentals of recommender systems, from collaborative filtering to deep learning approaches for personalized recommendations.
This course provides a comprehensive introduction to recommender systems, covering both traditional and modern approaches. Students will learn the basics of collaborative filtering, matrix factorization, and deep learning techniques applied to recommendation tasks. The curriculum includes hands-on experience with Python libraries like Surprise and Keras for building and evaluating recommender models. Learners will explore advanced topics such as handling large-scale data, addressing cold start problems, and improving scalability. By the end of the course, students will be equipped to design, implement, and evaluate recommender systems for various applications.
Instructors:
English
Powered by
What you'll learn
Understand the basic concepts and applications of recommender systems
Implement collaborative filtering techniques, including user-based and item-based approaches
Apply matrix factorization methods for improved recommendation accuracy
Develop deep learning models for recommendation tasks using Keras
Utilize the Surprise library for building and evaluating recommender systems
Assess recommender system performance using appropriate metrics
Skills you'll gain
This course includes:
2.88 Hours PreRecorded video
13 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This course provides a comprehensive exploration of recommender systems, covering both traditional and cutting-edge approaches. Students will learn the fundamentals of collaborative filtering, including user-based and item-based methods, as well as matrix factorization techniques. The curriculum then progresses to deep learning approaches for recommendation tasks, utilizing libraries such as Surprise and Keras for practical implementation. Learners will gain hands-on experience in building and evaluating recommender models, with a focus on performance metrics and algorithm comparison. The course also addresses advanced topics like processing large-scale data, handling cold start problems, and improving system scalability. Through a combination of theoretical concepts and practical exercises, students will develop the skills to design, implement, and optimize recommender systems for various real-world applications.
Introduction to Recommender Systems
Module 1 · 4 Hours to complete
Collaborative Filtering
Module 2 · 4 Hours to complete
Collaborative Filtering
Module 3 · 4 Hours to complete
Further Understanding of Recommender Systems
Module 4 · 4 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Assistant Professor at Sungkyunkwan University and Advocate for Faith and Student Support
Jaekwang Kim is an Assistant Professor at Sungkyunkwan University, affiliated with the School of Convergence, the Department of Computing, and the Department of Applied Data Science. He earned his B.S., M.S., and Ph.D. degrees from Sungkyunkwan University in 2004, 2006, and 2014, respectively. Dr. Kim's research focuses on artificial intelligence, particularly in recommendation algorithms and intelligent systems. He is also actively involved in campus ministry as a member of the University Bible Fellowship, supporting students in both their faith and academic pursuits.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.