By the end of this course, learners will be able to:

1. Understand the theory and importance of recommendation systems.
2. Implement user-based and item-based collaborative filtering.
3. Build content-based recommenders using text, tags, and features.
4. Apply matrix factorization and latent factor models.
5. Integrate deep learning models such as neural collaborative filtering (NCF).
6. Utilize embeddings and sequence models for personalized ranking.
7. Handle cold start, sparsity, and scalability challenges.
8. Evaluate model performance using precision, recall, MAP, and NDCG metrics.
9. Deploy and monitor recommendation engines in production.
10. Design hybrid recommendation architectures combining multiple approaches.

Course Syllabus

Module 1: Introduction to Recommendation Systems
Overview, history, and applications of recommendation technologies.

Module 2: Understanding User Behavior and Data
Data types, implicit vs. explicit feedback, and preprocessing techniques.

Module 3: Collaborative Filtering Fundamentals
User-based and item-based methods, similarity measures, and neighborhood models.

Module 4: Content-Based Filtering
Text and metadata-based recommendations using TF-IDF and cosine similarity.

Module 5: Matrix Factorization Techniques
SVD, ALS, and latent factor models for scalable recommendation.

Module 6: Hybrid Recommendation Systems
Combining collaborative, content, and context-based strategies.

Module 7: Deep Learning for Recommendations
Using neural networks, embeddings, and sequence models (RNNs, Transformers).

Module 8: Context-Aware and Graph-Based Recommendations
Temporal, session-based, and graph neural network (GNN)-based models.

Module 9: Evaluation Metrics and Model Optimization
Precision, recall, hit rate, MAP, and NDCG; hyperparameter tuning.

Module 10: Handling Cold Start and Scalability
Strategies for new users/items, data sparsity, and distributed training.

Module 11: Real-Time Recommendation Deployment
Building APIs and integrating with web or mobile apps.

Module 12: Capstone Project – Intelligent Recommendation System
Build and deploy a complete recommendation engine using a real-world dataset.

Upon successful completion, learners will receive a Certificate of Mastery in Recommendation Systems from Uplatz.

This certification validates your expertise in designing, implementing, and deploying intelligent recommendation engines that personalize digital experiences.

It demonstrates your ability to:

• Build and optimize recommendation algorithms using both traditional and deep learning techniques.
• Create scalable systems capable of handling real-world data.
• Deploy production-ready recommenders integrated with applications.

This credential confirms your readiness to contribute to projects in e-commerce, entertainment, social media, and AI-driven personalization, marking you as a skilled professional in one of the most impactful areas of applied machine learning.

Expertise in recommendation systems opens career paths in data-driven industries such as:

• Machine Learning Engineer
• Data Scientist (Personalization)
• Recommendation System Engineer
• AI Product Developer
• Research Scientist (Recommender Systems)

These skills are crucial across domains like retail, streaming, social platforms, gaming, and finance—where personalized recommendations directly drive business value.

1. What are the main types of recommendation systems?
   Collaborative filtering, content-based filtering, and hybrid systems.
2. What is the difference between user-based and item-based collaborative filtering?
   User-based finds similar users, while item-based finds similar items based on interaction history.
3. What is matrix factorization in recommendation systems?
   A technique that decomposes the user-item interaction matrix into latent features representing preferences.
4. What is the cold start problem?
   A situation where the system lacks sufficient data for new users or items to make accurate recommendations.
5. How do you evaluate recommendation system performance?
   Using metrics such as precision, recall, F1-score, MAP, and NDCG.
6. What are embeddings in recommendation models?
   Vector representations that capture semantic relationships between users and items.
7. What is a hybrid recommender system?
   A system that combines multiple recommendation techniques for improved accuracy and robustness.
8. How do deep learning models improve recommendations?
   By capturing nonlinear relationships and complex user-item interactions through neural architectures.
9. What tools and libraries are used to build recommendation systems?
   TensorFlow, PyTorch, Surprise, LightFM, implicit, and Scikit-learn.
10. Name real-world applications of recommendation systems.
    E-commerce product suggestions, movie and music recommendations, social media feeds, and news personalization.