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Recommender Systems
Master the fundamentals of recommender systems, from collaborative filtering to deep learning approaches for personalized recommendations.
Course Cost
₹ 2,699
Intermediate
Skill Level
16 Hours
Self-paced lessons
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.
English
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
Address challenges such as cold start problems and scalability issues
Gain practical experience in processing and analyzing large-scale recommendation data
Skills you'll gain
This course includes:
2.88 Hours PreRecorded video
13 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
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Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
CREATED BY
PROVIDED BY
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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
Recommendation Systems with Surprise and Deep Learning
Module 3 · 246 Minutes to complete
Further Understanding of Recommender Systems
Module 4 · 4 Hours to complete
Fee Structure
Payment options
Financial Aid
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Faculties
These are the expert instructors who will be teaching you throughout the course. With a wealth of knowledge and real-world experience, they're here to guide, inspire, and support you every step of the way. Get to know the people who will help you reach your learning goals and make the most of your journey.
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Frequently asked Questions
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