Course Summary
This course teaches advanced techniques for building recommender systems, including matrix factorization and factorization machines.Key Learning Points
- Learn how to build recommender systems using matrix factorization and factorization machines
- Understand how to handle implicit feedback data
- Explore advanced techniques for improving the performance of recommender systems
Related Topics for further study
- Matrix Factorization
- Factorization Machines
- Implicit Feedback Data
- Collaborative Filtering
- Deep Learning
Learning Outcomes
- Understand the principles behind matrix factorization and factorization machines
- Be able to implement advanced techniques for building recommender systems
- Improve the performance of recommender systems using implicit feedback data
Prerequisites or good to have knowledge before taking this course
- Familiarity with programming in Python
- Knowledge of machine learning basics
Course Difficulty Level
AdvancedCourse Format
- Online
- Self-paced
Similar Courses
- Machine Learning with Python
- Applied Data Science
- Deep Learning
Related Education Paths
Related Books
Description
In this course, you will see how to use advanced machine learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model.
Knowledge
- You will be able to use some machine learning and neural network techniques, in order to build more sophisticated recommender systems.
- You will learn how to combine different basic approaches into a hybrid recommender system, in order to improve the quality of recommendations.
- You will know how to integrate different kinds of side information (about content or context) in a recommender system.
- You'll learn how to use factorization machines and represent the input data, mixing together different kinds of filtering techniques.
Outline
- ADVANCED COLLABORATIVE FILTERING
- Course overview and welcome by the instructor
- Welcome by the instructor - module overview
- Item-Based CF as Optimization Problem
- SLIM
- Recap by the instructor
- Bayesian Probabilistic Ranking
- Conclusions by the instructor
- Course Syllabus
- Credits & Aknowledgements
- Module 1 Advanced - Graded Assessment
- SINGULAR VALUE DECOMPOSITION TECHNIQUES - SVD
- Welcome by the instructor
- Matrix Factorization
- Funk SVD
- SVD++
- Recap by the instructor
- Asymmetric SVD
- Pure SVD
- Conclusions by the instructor
- Module 2 Advanced - Graded Assessment
- HYBRID AND CONTEXT AWARE RECOMMENDER SYSTEMS
- Welcome by the instructor
- Hybrid Recommender Systems
- Linear Combination
- List Combination
- Pipelining
- Recap by the instructor
- Merging Models
- CF with Side Information
- Context-Aware Recommender Systems
- Conclusions by the instructor
- Module 3 Advanced - Graded Assessment
- FACTORIZATION MACHINES
- Welcome by the instructor
- Factorization Machines
- Recap by the instructor
- Explaining FM's Model
- Extending the model
- Solving the imbalance problem
- Conclusions by the instructor
- Module 4 Advanced - Graded Assessment
- Recsys Challenge (Honors)
- The RecSys Challenge
Summary of User Reviews
Key Aspect Users Liked About This Course
in-depth coverage of advanced topicsPros from User Reviews
- Great course content and structure
- Instructors are knowledgeable and engaging
- Real-world examples and case studies
- Highly relevant for professionals in the field
- Hands-on assignments and quizzes
Cons from User Reviews
- Some concepts may be challenging for beginners
- Limited interaction with instructors and peers
- Lack of practical implementation guidance
- Requires prior knowledge of basic recommender systems
- Some lectures can be lengthy and dense
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