AI Recommendation Systems Development Course

Lesson 1 • Data Landscape for Recommendations

Identifies the data types that feed recommendation systems and their quality requirements. Prepares students to audit and plan data pipelines.

Lesson 2 • What Recommendation Systems Do

Defines recommendation systems, their role in products, and the value they generate. Establishes shared vocabulary used throughout the course.

Lesson 3 • Core Recommendation Paradigms

Surveys collaborative filtering, content-based, and hybrid approaches. Connects each paradigm to the problem types it solves best.

Lesson 1 • User Profile Construction

Builds static and dynamic user profiles from interaction histories. Connects user behavior data types to model input representations.

Lesson 2 • Building Scalable Feature Pipelines

Designs batch and streaming pipelines that deliver features reliably at scale. Prepares students for production-grade data infrastructure.

Lesson 3 • Item Feature Extraction

Transforms raw item metadata into structured features usable by models. Extends the item metadata concepts from Chapter 1 into actionable engineering steps.

Lesson 1 • Memory-Based Collaborative Filtering

Implements user-user and item-item similarity methods using interaction matrices. Builds directly on the collaborative filtering paradigm introduced in Chapter 1.

Lesson 2 • Matrix Factorization Methods

Decomposes interaction matrices into latent factor representations for scalable recommendations. Introduces the linear algebra underpinning modern CF models.

Lesson 3 • Implicit Feedback Models

Adapts matrix factorization to implicit signals such as clicks and views. Extends the implicit feedback concepts from Chapter 2 into model training.

Lesson 1 • Text-Based Recommendation Models

Uses NLP techniques to recommend items based on textual descriptions and reviews. Extends TF-IDF and embedding concepts from Chapter 2 into recommendation pipelines.

Lesson 2 • Knowledge-Based Recommendation

Encodes domain constraints and user requirements into rule-driven recommenders. Addresses cold-start scenarios where interaction data is unavailable.

Lesson 3 • Content-Based Filtering Architecture

Constructs item profiles and user preference models from item features. Applies feature engineering skills from Chapter 2 to content-based model design.

Lesson 1 • Sequence-Aware Recommendation Models

Models user interaction sequences with RNNs and Transformer architectures. Extends session-based profile concepts from Chapter 2 into sequential deep models.

Lesson 2 • Graph Neural Networks for Recommendations

Applies graph convolutional networks to user-item interaction graphs. Extends knowledge graph concepts from Chapter 4 into end-to-end trainable GNN models.

Lesson 3 • Neural Embedding Models

Trains user and item embeddings using neural collaborative filtering frameworks. Builds on matrix factorization intuition from Chapter 3 with nonlinear capacity.

Lesson 4 • Autoencoders for Collaborative Filtering

Uses variational and denoising autoencoders to learn compact user representations. Connects implicit feedback modeling from Chapter 3 to generative approaches.

Lesson 1 • Context-Aware Recommendation Models

Integrates time, location, and device context into recommendation scoring. Extends user profile construction from Chapter 2 with dynamic contextual inputs.

Lesson 2 • Bandit Algorithms for Exploration

Applies multi-armed bandit methods to balance exploration and exploitation in recommendations. Introduces online learning as a complement to offline-trained models.

Lesson 3 • Real-Time Serving Infrastructure

Designs low-latency serving systems that retrieve and rank candidates in milliseconds. Connects feature pipeline skills from Chapter 2 to production serving architecture.

Lesson 1 • Debiasing and Mitigation Techniques

Applies inverse propensity scoring, re-ranking, and adversarial training to reduce bias. Provides actionable remediation tools for models built in earlier chapters.

Lesson 2 • Sources of Bias in Recommendations

Catalogs popularity bias, exposure bias, and feedback loops that distort recommendations. Grounds bias analysis in the data and model types covered in prior chapters.

Lesson 3 • Fairness Metrics and Auditing

Defines group and individual fairness metrics and applies them to recommendation outputs. Extends evaluation frameworks from Chapter 1 to fairness dimensions.

Lesson 1 • Monitoring and Drift Detection

Tracks model performance, data drift, and system health in live recommendation systems. Connects data quality monitoring from Chapter 2 to production observability.

Lesson 2 • Model Packaging and Serving

Packages trained models into containerized services with versioned APIs. Operationalizes the serving infrastructure concepts introduced in Chapter 6.

Lesson 3 • Continuous Training and Retraining

Automates model retraining pipelines triggered by drift signals or scheduled intervals. Ensures recommendation systems remain accurate as user behavior evolves.

Lesson 4 • Online Experimentation and A/B Testing

Designs statistically valid A/B and interleaving experiments for recommendation changes. Extends the A/B testing fundamentals from Chapter 1 to production experiment design.