Recommendation Systems with Machine Learning
Course Summary
This course provides a practical introduction to recommendation systems, exploring collaborative filtering, content-based filtering, and hybrid recommendation models. Participants will learn how to build personalized recommendation engines using matrix factorization techniques (SVD, NMF), deep learning-based recommenders, and reinforcement learning approaches. The course also covers real-world applications in e-commerce, media streaming, and personalized content recommendations, using Python, Scikit-learn, and TensorFlow.
Key Learning Outcomes
- Understand the fundamentals of recommendation systems and their real-world applications.
- Implement collaborative filtering techniques (user-based and item-based recommendations).
- Build content-based filtering models using NLP and feature extraction techniques.
- Apply matrix factorization (SVD, NMF) and deep learning approaches (autoencoders, neural networks) for recommendations.
- Learn evaluation metrics (precision, recall, RMSE) for assessing recommendation performance.
