Recommendation Systems Masterclass

A 28-hour masterclass on building recommendation engines — from collaborative filtering and matrix factorization to deep autoencoder and similarity-based models. You learn how personalization actually drives engagement, revenue and retention, and how to evaluate a recommender before it ships.

Through hands-on projects you build recommenders on real interaction data — from classic collaborative filtering and matrix factorization to deep autoencoder models — and measure them with the right offline metrics. The capstone is a working recommendation engine.

• Data scientists building personalization and recommendation features.
• E-commerce and product teams improving engagement.

• Build collaborative-filtering and content-based recommenders.
• Apply matrix factorization to large interaction data.
• Design deep recommenders with Keras and autoencoders.
• Evaluate and tune recommendations with the right metrics.
• Ship personalization that lifts conversion and retention.

• Hands-on labs throughout the program (70% project-based).
• A capstone project demonstrating end-to-end skills.

• Design and evaluate recommender systems on real interaction data.
• Ship personalization that lifts conversion and retention.

Globally accredited certificate issued by Epsilon AI Learning — USA, with a unique Certificate ID and EPSILON ID. Awarded on 80% attendance, 80% final exam, and a capstone project.

• Where and why recommendations matter
• Explicit vs. implicit feedback
• Evaluation metrics for recommenders

• User- and item-based methods
• Similarity measures
• Cold-start challenges

• Latent factors and embeddings
• SVD and ALS
• Regularization and tuning

• Content-based recommendations
• Hybrid recommenders
• Handling sparse data

• Neural collaborative filtering
• Autoencoders for recommendation
• Building with Keras

• Framing a recommendation problem
• Training and evaluating the engine
• Serving recommendations