Recommender Systems
From collaborative filtering to generative recommenders
About this program
Six months on building recommendation systems at scale. From matrix factorization through two-tower retrieval, gradient-boosted ranking, sequence models, and the rise of LLM-based recommenders. Includes deployment patterns and the privacy realities of Canadian recsys.
Student ratings
Highly rated — 267 verified Canadian graduates rated this program 4.6/5. Reviews emphasize the applied capstone, instructor responsiveness, and career outcomes.
- 5★173
- 4★87
- 3★5
- 2★1
- 1★1
Who this program is for
- →Practitioners already shipping ml engineering work who want depth
- →Senior engineers, data scientists, and technical leads
- →Canadian residents seeking a verifiable diploma credential
Topics you'll cover
6 modules across 6 months — 24 lessons in total.
Full syllabus
Module 1 · Month 1 — Foundations▾
- L1Problem framing
- L2Implicit vs. explicit feedback
- L3Evaluation metrics
- L4Baselines
Module 2 · Month 2 — Classical Methods▾
- L1Matrix factorization
- L2ALS and BPR
- L3Content-based
- L4Hybrid systems
Module 3 · Month 3 — Two-Tower Retrieval▾
- L1Embedding-based retrieval
- L2Negative sampling
- L3ANN indexes
- L4Lab: implementation
Module 4 · Month 4 — Ranking▾
- L1GBDT ranking
- L2Neural ranking
- L3Listwise losses
- L4Multi-objective
Module 5 · Month 5 — Sequence and Generative▾
- L1SASRec and BERT4Rec
- L2Generative retrieval
- L3LLM-based recommenders
- L4Cold start strategies
Module 6 · Month 6 — Capstone▾
- L1End-to-end recommender
- L2Online experiment design
- L3Privacy-aware deployment
- L4Final review
What you'll be able to do
- ●Build candidate retrieval and ranking stacks
- ●Implement two-tower architectures
- ●Use sequence models for next-item prediction
- ●Apply LLMs to recommendation
- ●Run rigorous A/B tests
Career paths after graduation
Frequently asked questions
How much does the Recommender Systems cost?▾
Tuition is $175 CAD, paid once. You can pay in full at checkout or choose an interest-free monthly plan. A 30-day refund window applies from your enrollment date.
How long is the Recommender Systems program?▾
Self-paced. Most students complete it in 6 months at roughly 8 hours per week, but you can go faster or slower — you keep lifetime access.
What are the prerequisites?▾
Strong Python; Deep learning fundamentals
Is the diploma recognized in Canada?▾
Yes. Graduates receive the Altaris AI Academy Diploma in ML Engineering — a verifiable credential with a unique certificate number you can publish on LinkedIn and that any employer can verify at altarisai.org/verify.
What is the refund policy?▾
Full refund within 30 days of enrollment, no questions asked. After day 30, prorated refunds are available per our Refund Policy.
Who teaches the program?▾
Working Canadian AI practitioners — not academics. Every module is built and reviewed by a lead instructor working in the field today.
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More ML Engineering programs from Altaris.