Master recommender systems with AI: collaborative filtering, matrix factorization, two-tower retrieval, transformers, LLMs, evaluation, GDPR and DSA.
The course’s skills. The same ones appear on your Certificate of Completion, so you can show what you covered.
A premium, complete and hands-on course on modern recommender systems, updated for 2026. You will start from why recommendation is one of the highest-leverage applications of machine learning, then build up every major family of models with real Python code. You will master content-based filtering with TF-IDF and embeddings, memory-based collaborative filtering (user-based and item-based neighborhoods), and matrix factorization with SVD, ALS and Bayesian Personalized Ranking using the implicit and LightFM libraries. You will learn the crucial difference between explicit and implicit feedback and why it changes both your model and your loss. From there you will build deep learning recommenders — neural collaborative filtering, embeddings, and the two-tower retrieval architecture — and sequential and session-based models with self-attention (SASRec, BERT4Rec). You will design the two-stage candidate-generation-plus-ranking architecture that powers recommendation at scale, serve it with approximate nearest neighbor search, and solve the cold-start problem. You will evaluate systems correctly with precision@k, recall@k, MAP and NDCG, understand why offline and online metrics diverge, and run trustworthy A/B tests. The final modules cover how large language models reshape recommendation in 2026, how to build for diversity, serendipity and fairness, and how to stay compliant with the GDPR when you profile user behavior. Includes a comprehensive final assessment. This deepened edition adds factorization machines, graph neural networks (LightGCN, PinSage), multi-armed and contextual bandits with off-policy evaluation, and a dedicated lesson on the EU Digital Services Act's recommender transparency duties - around 30 in-depth lessons of university-level, practice-first material.
For developers, engineers and technical professionals who build or integrate AI solutions. It belongs to Deep Learning & AI Models, on the IT track.
Advanced level: assumes hands-on experience with AI in the course’s field.
11 modules · 30 lessons · about 25 hours · 355 quiz questions
Modules and lessons in order, plus the link to the preview of lesson 1 — so you can decide at your own pace.
You read the lesson, ask the AI Professor right on the page and check what you remember.
Why Recommender Systems Matter in 2026
Every day, billions of people interact with recommender systems without ever naming them. The film you were nudged toward, the next track that started playing on its own, the product placed at the top of a store page, the post at the top of a feed, the code completion your editor suggested, and the document your enterprise search surfaced first — all of these are recommendations.
Written lessons, with examples and exercises. Your progress is saved automatically, so you can pick up where you left off.
Select a passage and it explains it, gives you an example or tells you why it matters. You can type or talk to it, in English.
Every lesson has its own quiz, with explanations for the answers: 355 questions across the course. The professor also summarizes the lesson and prepares review flashcards.
Once you complete the lessons and reach the minimum quiz score, you issue it from your account, with a unique number, publicly verifiable.
The AI Professor is included in your subscription, per-course access included, with a monthly fair-use limit. It is an AI system, not a human teacher, and its answers may contain errors; details in the Terms. See how the AI Professor works.
The Certificate of Completion for “Recommender Systems with AI: From Collaborative Filtering to Deep Learning”: a private attestation with a unique number and a QR code. A recruiter confirms it is authentic on the public verification page, with no account and no cost.
Public verification page, QR code and cryptographic fingerprint, with no account for the person checking.
Add it to your CV and LinkedIn profile; a recruiter confirms authenticity with one click.
The course, the tier you earned (Pass, Merit or Distinction), the serial number and the issue date, plus the percentage of lessons completed.
A private attestation of course completion. It is NOT a diploma or a state-recognised qualification under Romanian Laws 198/2023, 199/2023 or Government Ordinance 129/2000; employers may take it into account at their own discretion. Certification policy.
A subscription by number of courses: 1, 3, 5, 10, 15 or 25 courses a month, or the full catalog. You pick them and can change them at every renewal; your progress and certificates stay. Billed monthly or yearly (12 months for the price of 10), in EUR. You can stop the renewal anytime from your account, and access starts right after payment. Prices are totals, with VAT included at the rate of your country.
This course goes first on your list; you pick the others in your account and can change them at every renewal.
€99 a course
€99a month €990a year, 12 months of access
under €67 a course
€199a month €1,990a year, 12 months of access
under €52 a course
€259a month €2,590a year, 12 months of access
under €32 a course
€319a month €3,190a year, 12 months of access
under €24 a course
€359a month €3,590a year, 12 months of access
under €16 a course
€399a month €3,990a year, 12 months of access
under €10 a course, across all 50
€499a month €4,990a year, 12 months of access
Total prices, VAT included. Move up anytime and pay only the difference for the days left; move down at renewal. Renews automatically, you stop it anytime from your account; your progress and certificates stay. Digital content with immediate access: see the withdrawal terms.
About the course, the price, the AI Professor and the certificate.
A premium, complete and hands-on course on modern recommender systems, updated for 2026. You will start from why recommendation is one of the highest-leverage applications of machine learning, then build up every major family of models with real Python code. You will master content-based filtering with TF-IDF and embeddings, memory-based collaborative filtering (user-based and item-based neighborhoods), and matrix factorization with SVD, ALS and Bayesian Personalized Ranking using the implicit and LightFM libraries. You will learn the crucial difference between explicit and implicit feedback and why it changes both your model and your loss. From there you will build deep learning recommenders — neural collaborative filtering, embeddings, and the two-tower retrieval architecture — and sequential and session-based models with self-attention (SASRec, BERT4Rec). You will design the two-stage candidate-generation-plus-ranking architecture that powers recommendation at scale, serve it with approximate nearest neighbor search, and solve the cold-start problem. You will evaluate systems correctly with precision@k, recall@k, MAP and NDCG, understand why offline and online metrics diverge, and run trustworthy A/B tests. The final modules cover how large language models reshape recommendation in 2026, how to build for diversity, serendipity and fairness, and how to stay compliant with the GDPR when you profile user behavior. Includes a comprehensive final assessment. This deepened edition adds factorization machines, graph neural networks (LightGCN, PinSage), multi-armed and contextual bandits with off-policy evaluation, and a dedicated lesson on the EU Digital Services Act's recommender transparency duties - around 30 in-depth lessons of university-level, practice-first material.
The course has 11 modules and 30 lessons, with 355 quiz questions. The full curriculum, module by module, is shown on the course page.
Yes. The course is Advanced level and assumes hands-on experience with AI in its field.
The estimated duration is about 25 hours, across 30 lessons. You learn at your own pace, and your progress is saved automatically at every lesson, so you can pick up where you left off.
This course alone: the 1 course plan, €99 per month. The course fits into every plan of the subscription, next to other courses you choose: for example 3 courses for €199, 10 courses for €319 (recommended) or the full catalog for €499 per month.
You pick your courses after paying and can change them at every renewal; starting from here, this one is already picked. Plans are billed in EUR, monthly or yearly (pay for 10 months, get 12). Every price is a total, with VAT included at the rate of your country. See all plans.
Yes. You can read a free preview of lesson 1, “Why Recommender Systems Matter in 2026”, without an account. The preview shows you how the lesson is written; the rest of the lesson and the other lessons open after payment.
The AI Professor is built into every lesson and starts from the lesson you have open: it explains the passages you select, gives you examples, summarizes the lesson, tests you with a quiz and prepares review flashcards. You can type or talk to it, in English.
It is included in every plan of your subscription, with a fair-use limit. It is an AI system, not a human teacher, and its answers may contain errors; details in the Terms.
When you meet the completion thresholds shown on the platform (lessons completed and the minimum quiz score), you can issue your Certificate of Completion for “Recommender Systems with AI: From Collaborative Filtering to Deep Learning” from your account, with a unique number, publicly verifiable on the site.
It is a private attestation of course completion, issued by GALIAN SOFTWARE DEVELOPMENT SRL. It is NOT a diploma or a state-recognised qualification under Romanian Laws 198/2023, 199/2023 or Government Ordinance 129/2000; employers may take it into account at their own discretion. Details in the Certification policy.
Yes. The subscription renews automatically at the end of each monthly billing period. You can see the next renewal date in your account at any time.
You can stop the renewal at any time, from your account or by email, with no other formality. Access stays active until the end of the period already paid for, and no pro-rata refund is made for a period already started, except in the cases provided by law. Your progress stays saved in your account. Details in the Terms & Conditions, section 5.5.
Yes. With the team library, the company admin picks 3, 5, 10, 15 or 25 courses a month, or the full catalog, for the whole team, from €49 per member per month, excl. VAT, for 5 members or more. A dashboard shows each member’s progress and manages seats centrally. Price it for your team.
You can pick them together with this one in a 3, 5 or 10-course subscription (3 courses for €199 a month), and the full catalog includes them all. A single course is €99 a month.
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“Recommender Systems with AI: From Collaborative Filtering to Deep Learning”: 30 lessons and about 25 hours of content, with your progress saved at every lesson.
1-course subscription
€99/month
VAT included · stop the renewal anytime from your account
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