Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery |
|---|---|---|
| English | Self Study | Video and Text Based |
Course Overview
The Nearest Neighbor Collaborative Filtering certification course is a joint online offering of the University of Minnesota, and Coursera. There are 2 different instructors of this particular course namely Joseph A Konstan, and Michael D. Ekstrand. The course is basically part 2 of 5 of the Recommender Systems Specialization certification programme.
The Nearest Neighbor Collaborative Filtering training is of 4 weeks duration only that is only made of videos, quizzes, and peer review, and feedback. This algorithms programme is accompanied by a completion certificate separately designed only for this course that can be shared with hiring companies, and online job portals. Also, interestingly the deadlines for this course can be fixed depending upon the students own free will.
The Highlights
- Online course
- Shareable certificate
- 15 hours for completion
- Different course subtitles available
Programme Offerings
- Flexible Deadlines
- Short Programme
- Many Subtitles
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | University of Minnesota, MinneapolisCoursera |
The Nearest Neighbor Collaborative Filtering certification fee is based on the recommended pace of learning. It depends, and varies between the different places like it may be 1 month, 3 months or even 6 months. All the paces shall include a completion certificate. The details are shared in the table format down below:
Nearest Neighbor Collaborative Filtering Fee Details
Description | Amount |
1 Month | Rs. 3,992/month |
Eligibility Criteria
Certification Qualifying Details
The Nearest Neighbor Collaborative Filtering certification by Coursera is offered when all the assignments, and quizzes are done by the participants.
What you will learn
The Nearest Neighbor Collaborative Filtering certification syllabus will help the candidates implement user-algorithm variations. The candidates will also be able to provide personalized recommendations that are based upon a user’s own ratings.
Who it is for
The Nearest Neighbor Collaborative Filtering certification course shall be beneficial for engineers, and people associated with the computer science background.
Admission Details
To get admission to the Nearest Neighbor Collaborative Filtering classes, the students can follow these steps:
Step 1: Follow the official URL: https://www.coursera.org/learn/collaborative-filtering.
Step 2: Find the ‘Enrol Now’ written button.
Step 3: After the students select their desired pace of learning they may go ahead, and submit the fee to get admission.
The Syllabus
Video
- Course Introduction
Reading
- Course Structure Outline
Videos
- 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
Videos
- Assignment Introduction
- Programming Assignment - Programming User-User Collaborative Filtering
Readings
- Assignment Instructions: User-User CF
- Introducing User-User CF Programming Assignment
Practice Exercises
- User-User CF Answer Sheet
- User-User Collaborative Filtering Quiz
Videos
- 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
Videos
- Item-Based CF Assignment Intro Video
- Programming Assignment - Programming Item-Item Collaborative Filtering
Readings
- Item-Based CF Assignment Instructions
- Introducing Item-Item CF Programming Assignment
Practice Exercises
- Item Based Assignment Part l
- Item Based Assignment Part II
- Item Based Assignment Part III
- Item Based Assignment Part IV
Videos
- The Cold Start Problem
- Recommending for Groups: Interview with Anthony Jameson
- Threat Models
- Explanations
- Explanations, Part II: Interview with Nava Tintarev
Practice Exercise
- Item-Based and Advanced Collaborative Filtering Topics Quiz
Instructors
University of Minnesota, Minneapolis Frequently Asked Questions (FAQ's)
The average rating shared in Coursera is 4.3.
The maximum number of hours is 15.
Already more than 13,000 people have signed up.
The 2 instructors are Joseph A Konstan, and Michael D. Ekstrand.
Yes, just to check out or experience the students may opt in this single course.
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