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
Machine Learning finds widespread application in providing recommendations and making better predictions in software by deploying opinions from users and constructing a model automatically while eliminating the need to consider all the details of that model. Advanced Recommender Systems Certification by Coursera will expose learners to advanced machine learning techniques that can be used to create sophisticated recommender systems.
Offered by EIT Digital and Politecnico di Milano, both of which are top-ranked in their respective domain, this course boosts one's creativity and innovation skills and implores them to think beyond the thinkable. Advanced Recommender Systems Certification Syllabus will deal with the management of hybrid information and a combination of various filtering techniques. This course will ensure that learners derive the best from each approach taught to them.
At the course conclusion, candidates will have been well acquainted with factorisation machines and how to represent input data accordingly. They will learn all the essential methods that can be used to solve the cross-domain recommendation problem. Finally, identifying new challenges and trends in giving recommendations for numerous innovative application contexts will also be highlighted in Advanced Recommender Systems Certification Course.
The Highlights
- MOOC offering by Politecnico di Milano and EIT Digital
- Learn at your own pace
- Free course material
- 14 hours of online learning
- Online certification
- Coursera is a course provider
Programme Offerings
- videos
- quizzes
- Practice Exercises
- Readings
- Instructor-based learning
- online learning
- Self-paced mode.
Courses and Certificate Fees
| Fees Informations | Certificate Availability | Certificate Providing Authority |
|---|---|---|
| INR 2435 | yes | Coursera |
The fees for the course Advanced Recommender Systems is -
| Head | Amount in INR |
| Certificate fees | Rs. 2,435 |
Eligibility Criteria
Education
Students willing to pursue Advanced Recommender Systems Certification Course must have a fair knowledge of recommender systems. Moreover, an academic familiarity with basic programming languages like Python and linear algebra concepts is also required.
Certification qualifying details
Advanced Recommender Systems Certification shall be meted out to paid learners of this course who ensure that they have been verified on the Coursera platform after registration and pass all their graded assignments successfully.
What you will learn
Advanced Recommender Systems Certification Programme is an intermediate-level course for eligible learners who want to brush up on their machine learning knowledge and learn more about recommender systems. It will enhance the knowledge of learners by teaching them the following-
- Candidates will be able to adopt machine learning in collaborative filtering techniques.
- They will learn how to use factorisation machines while representing the input data and mixing various kinds of filtering techniques.
- They will be able to mark the difference between model-based and memory-based and recommender systems along with their pros and cons.
- They shall also learn how basic approaches combine to form a hybrid recommender system that improves the quality of recommendations.
- Learners shall learn to define a new error metric based on ranking comparisons that shall play an instrumental role in designing learning-to-rank algorithms.
- Machine learning and neural network techniques will be taught in detail to design more sophisticated recommender systems.
- Learners will know how to balance input information using coefficients and weights for creating more sophisticated predictions.
- They will be able to integrate many kinds of side information (about content or context) in a recommender system.
- Candidates shall study different simple and complex hybridisation approaches.
- They shall get a practical insight on how to choose the accurate number of latent features to reduce the risk of overfitting historical data.
Admission Details
Advanced Recommender Systems Certification Online Course is accepting admissions online through the course page. Learners can enrol by following the given steps-
Step 1: Select the option for enrolling on the course link- https://www.coursera.org/learn/advanced-recommender-systems
Step 2: Choose the mode of learning-free course or paid course.
Step 3: Pay the due certification fee through the payment portal if you want the certificate.
Step 4: If you select the free course, you will get direct access to the course.
The Syllabus
Videos
- 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
Readings
- Course Syllabus
- Credits & Aknowledgements
Assignment
- Module 1 Advanced - Graded Assessment
Peer Reviews
- SLIM
- BPR
Videos
- Welcome by the instructor
- Matrix Factorization
- Funk SVD
- SVD++
- Recap by the instructor
- Asymmetric SVD
- Pure SVD
- Conclusions by the instructor
Assignment
- Module 2 Advanced - Graded Assessment
Peer Review
- Recommending items
Discussion Prompt
- Explainability in Machine Learning
Videos
- 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
Assignment
- Module 3 Advanced - Graded Assessment
Peer Review
- Tensor-based factorization
Discussion Prompts
- Preferences in context
- A matter of weights
Videos
- 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
Assignment
- Module 4 Advanced - Graded Assessment
Peer Review
- Factorization Machines
Discussion Prompts
- Multimedia contents
Reading
- The RecSys Challenge
Programming Assignment
- RecSys Challenge on Kaggle
Instructors
Polytechnic University of Milan, Milan Frequently Asked Questions (FAQ's)
Week 5 comprises a practice exercise that is optional and can be undertaken by learners who want an Honors designation on their course certificate.
Once the candidate enrolls in the programme, he will be able to check the deadline on his course dashboard.
Both course videos and readings can be accessed by the learners who enroll for free. They will have to pay to access the assignments.
Candidates have to enter their annual household income in USD (US Dollars).
All videos have subtitles and transcripts so that learners with hearing impairments can understand.
Quizzes in this course can be taken as many times as the learner wants.
Honors assignment is not necessary to receive the course certificate.
An assignment will be locked if: the payment has expired, the assignment has not yet been activated, if a previous assignment has not been completed.
Yes, the email enclosing the result will also state the reason for refusal so that the learner may know what went wrong and apply again.
No, interested applications will have to enrol for the course. They can opt-out of the course later if they want.