Course Summary
Collaborative Filtering is a powerful technique for building personalized recommendation systems. This course teaches the fundamentals of Collaborative Filtering and its applications in real-world scenarios.Key Learning Points
- Understand the principles of Collaborative Filtering
- Learn how to build recommendation systems using Collaborative Filtering
- Apply Collaborative Filtering to real-world problems
Related Topics for further study
Learning Outcomes
- Develop a strong understanding of Collaborative Filtering
- Build personalized recommendation systems using Collaborative Filtering
- Apply Collaborative Filtering to real-world scenarios
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Machine Learning
- Familiarity with Python programming
Course Difficulty Level
IntermediateCourse Format
- Online Self-Paced
- Video Lectures
- Programming Assignments
- Quizzes
Similar Courses
- Recommender Systems and Deep Learning in Python
- Applied Data Science: Machine Learning
- Machine Learning for Business Professionals
Related Education Paths
Notable People in This Field
- Yann LeCun
- Andrew Ng
- Geoffrey Hinton
Related Books
Description
In this course, you will learn the fundamental techniques for making personalized recommendations through nearest-neighbor techniques. First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. You will explore and implement variations of the user-user algorithm, and will explore the benefits and drawbacks of the general approach. Then you will learn the widely-practiced item-item collaborative filtering algorithm, which identifies global product associations from user ratings, but uses these product associations to provide personalized recommendations based on a user's own product ratings.
Outline
- Preface
- Course Introduction
- Course Structure Outline
- User-User Collaborative Filtering Recommenders Part 1
- User-User Collaborative Filtering
- Configuring User-User Collaborative Filtering
- Influence Limiting and Attack Resistance; Interview with Paul Resnick
- Trust-Based Recommendation; Interview with Jen Golbeck
- Impact of Bad Ratings; Interview with Dan Cosley
- User-User Collaborative Filtering Recommenders Part 2
- Assignment Introduction
- Programming Assignment - Programming User-User Collaborative Filtering
- Assignment Instructions: User-User CF
- Introducing User-User CF Programming Assignment
- User-User CF Answer Sheet
- User-User Collaborative Filtering Quiz
- Item-Item Collaborative Filtering Recommenders Part 1
- Introduction to Item-Item Collaborative Filtering
- Item-Item Algorithm
- Item-Item on Unary Data
- Item-Item Hybrids and Extensions
- Strengths and Weaknesses of Item-Item Collaborative Filtering
- Interview with Brad Miller
- Item-Item Collaborative Filtering Recommenders Part 2
- Item-Based CF Assignment Intro Video
- Programming Assignment - Programming Item-Item Collaborative Filtering
- Item-Based CF Assignment Instructions
- Introducing Item-Item CF Programming Assignment
- Item Based Assignment Part l
- Item Based Assignment Part II
- Item Based Assignment Part III
- Item Based Assignment Part IV
- Advanced Collaborative Filtering Topics
- The Cold Start Problem
- Recommending for Groups: Interview with Anthony Jameson
- Threat Models
- Explanations
- Explanations, Part II: Interview with Nava Tintarev
- Item-Based and Advanced Collaborative Filtering Topics Quiz
Summary of User Reviews
Discover the power of collaborative filtering with this course on Coursera. Students have raved about this course, praising its engaging content and practical applications. Gain a comprehensive understanding of the topic, without feeling overwhelmed by technical jargon.Key Aspect Users Liked About This Course
Many users were impressed with the practical applications of the course content.Pros from User Reviews
- Engaging and informative content
- Great for beginners and experts alike
- Practical applications of course content
- Excellent instructor with a thorough understanding of the subject
Cons from User Reviews
- Some users found the course too basic
- A few technical glitches in the course materials
- Not enough hands-on exercises
- Limited interaction with other students
Joseph A Konstan
4.3 Raiting
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