Matrix Factorization and Advanced Techniques
- 4.3
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Course Summary
Learn how to use matrix factorization to analyze data and make predictions. This course covers the theory and implementation of matrix factorization, as well as its applications in recommendation systems and image processing.Key Learning Points
- Understand the mathematical theory behind matrix factorization
- Learn how to implement matrix factorization using Python
- Explore the applications of matrix factorization in recommendation systems and image processing
Job Positions & Salaries of people who have taken this course might have
- USA: $113,309
- India: ₹1,023,137
- Spain: €42,702
- USA: $113,309
- India: ₹1,023,137
- Spain: €42,702
- USA: $112,380
- India: ₹1,130,000
- Spain: €38,000
- USA: $113,309
- India: ₹1,023,137
- Spain: €42,702
- USA: $112,380
- India: ₹1,130,000
- Spain: €38,000
- USA: $99,301
- India: ₹1,320,000
- Spain: €36,000
Related Topics for further study
Learning Outcomes
- Ability to understand the mathematical theory behind matrix factorization
- Proficiency in implementing matrix factorization using Python
- Knowledge of the applications of matrix factorization in recommendation systems and image processing
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of linear algebra
- Familiarity with Python programming
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Applied Data Science with Python
- Machine Learning
- Recommender Systems
Related Education Paths
Notable People in This Field
- Yann LeCun
- Andrew Ng
- Geoffrey Hinton
Related Books
Description
In this course you will learn a variety of matrix factorization and hybrid machine learning techniques for recommender systems. Starting with basic matrix factorization, you will understand both the intuition and the practical details of building recommender systems based on reducing the dimensionality of the user-product preference space. Then you will learn about techniques that combine the strengths of different algorithms into powerful hybrid recommenders.
Outline
- Preface
- Matrix Factorization and Advanced Techniques
- Matrix Factorization (Part 1)
- Introduction to Matrix Factorization and Dimensionality Reduction
- Singular Value Decomposition
- Gradient Descent Techniques
- Deriving FunkSVD
- Probabilistic Matrix Factorization
- On Folding-In with Gradient Descent
- Matrix Factorization (Part 2)
- Assignment Introduction
- Programming Matrix Factorization
- Assignment Instructions
- Intro - Programming Matrix Factorization
- Matrix Factorization Assignment Part l
- Matrix Factorization Assignment Part ll
- Matrix Factorization Assignment Part lll
- Matrix Factorization Quiz
- SVD Programming Eval Quiz
- Hybrid Recommenders
- Hybrid Recommenders
- Hybrids with Robin Burke
- Hybridization through Matrix Factorization
- Matrix Factorization Hybrids with George Karypis
- Interview with Arindam Banerjee
- Interview with Yehuda Koren
- Advanced Machine Learning
- Learning Recommenders
- Learning to Rank: Interview with Xavier Amatriain
- Personalized Ranking (with Daniel Kluver)
- Advanced Topics
- Context-Aware Recommendation I : Interview with Francesco Ricci
- Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 1)
- Context-Aware Recommendation II: Interview with Bamshad Mobasher (Part 2)
- Industry Practical Issues: Inteview with Anmol Bhasin
- Recommending Music - Interview with Paul Lamere
- Specialization Wrap Up
- Programming Hybrids & Learning to Rank
- Programming Hybrids and Machine Learning Description
- Hybrid and Advanced Techniques Quiz
- Honors Hybrid Assignment Evaluation Quiz
Summary of User Reviews
The Matrix Factorization course on Coursera is highly recommended by users. The course offers a comprehensive understanding of the topic and is taught by knowledgeable instructors. One key aspect that users found good about the course is its practical application and hands-on approach to learning.Pros from User Reviews
- Comprehensive understanding of the topic
- Knowledgeable instructors
- Practical application and hands-on approach to learning
- Flexible learning schedule
- Plenty of resources available
Cons from User Reviews
- Some users found the course to be too basic
- The course could benefit from more challenging assignments
- The discussion forums could be more active
- Some users found the course to be too theoretical
- The course could benefit from more real-world examples
Michael D. Ekstrand
4.3 Raiting
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- 4.4
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- 0.0
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