Course Summary
Learn how to build basic recommender systems using Python, and explore popular recommendation algorithms including Collaborative Filtering and Content-Based Filtering.Key Learning Points
- Understand the fundamentals of recommender systems and their application in different industries
- Learn how to build Collaborative Filtering and Content-Based Filtering systems using Python
- Explore matrix factorization techniques and evaluate the performance of your recommender system
Related Topics for further study
- Collaborative Filtering
- Content-Based Filtering
- Matrix Factorization
- Python Programming
- Recommender System Evaluation
Learning Outcomes
- Build basic recommender systems using Collaborative Filtering and Content-Based Filtering
- Apply matrix factorization techniques to improve the performance of your recommender system
- Evaluate the effectiveness of your recommender system using popular metrics such as RMSE and MAE
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with linear algebra and probability theory
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures and quizzes
- Programming assignments and peer-reviewed projects
Similar Courses
- Advanced Recommender Systems
- Machine Learning with Python
Related Education Paths
Notable People in This Field
- Research Scientist
- Professor
Related Books
Description
This course introduces you to the leading approaches in recommender systems. The techniques described touch both collaborative and content-based approaches and include the most important algorithms used to provide recommendations. You'll learn how they work, how to use and how to evaluate them, pointing out benefits and limits of different recommender system alternatives.
Knowledge
- You'll be able to build a basic recommender system.
- You'll be able to choose the family of recommender systems that best suits the kind of input data, goals and needs.
- You'll learn how to identify the correct evaluation activities to measure the quality of a recommender system, based on goals and needs.
- You'll be able to point out benefits and limits of different techniques for recommender systems in different scenarios.
Outline
- BASIC CONCEPTS
- Course overview and welcome by the instructor
- Welcome by the instructor - module overview
- Introduction to Recommender Systems
- Taxonomy of Recommender Systems
- Item-Content Matrix
- User-Rating Matrix
- Inferring Preferences
- Recap by the instructor
- Non-personalized Recommender Systems
- Global Effects
- Conclusions by the instructor
- Course Syllabus
- Credits & Acknowledgements
- Module 1 - Graded Assessment
- EVALUATION OF RECOMMENDER SYSTEMS
- Welcome by the instructor - module overview
- Quality of Recommender Systems
- Quality Indicators
- Online Evaluation Techniques
- Offline Evaluation Techniques
- Dataset Partitioning
- Overfitting
- Recap by the instructor
- Error Metrics
- Classification Metrics
- Ranking Metrics
- Conclusions by the instructor
- Module 2 - Graded Assessment
- CONTENT-BASED FILTERING
- Welcome by the instructor - module overview
- Content-based Filtering
- Cosine Similarity
- Matrix Notation
- K-Nearest Neighbours
- Recap by the instructor
- Improving the ICM
- TF-IDF
- Conclusions by the instructor
- Module 3 - Graded Assessment
- COLLABORATIVE FILTERING
- Welcome by the instructor - module overview
- Collaborative Filtering
- User-based CF
- Recap by the instructor
- Item-based CF
- User-based vs. Item-based
- Model-based vs. Memory-based
- Recommendation as Association Rules
- Conclusions by the instructor
- Module 4 - Graded Assessment
Summary of User Reviews
Learn about basic recommender systems with this Coursera course. The course has received positive reviews from users who appreciate the clear explanations and hands-on assignments. Overall, users find the course helpful and informative.Key Aspect Users Liked About This Course
The hands-on assignments are a key aspect that many users thought was good.Pros from User Reviews
- Clear explanations
- Hands-on assignments
- Helpful instructors
- Practical examples
- Good pacing
Cons from User Reviews
- Some topics are too basic
- Could use more advanced material
- Not very interactive
- Some technical difficulties
- Could be more engaging
Paolo Cremonesi
3.9 Raiting
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