Description
Recommender Systems and Deep Learning in Python
Recommendation engines power Netflix, Amazon, and Spotify. This course teaches you to build them—from **classical methods** like collaborative filtering to **modern deep learning** approaches using **Restricted Boltzmann Machines (RBMs)** and **matrix factorization**.
Projects You’ll Build
- A movie recommender using MovieLens dataset (like Netflix)
- A music recommendation engine using collaborative filtering
- A deep learning recommender with RBMs for implicit feedback
- A scalable Spark-based recommender for big data
Key Techniques Covered
- User-based and item-based collaborative filtering
- Matrix factorization—SVD, ALS, and neural matrix factorization
- Restricted Boltzmann Machines (RBMs)—for unsupervised feature learning
- Evaluation metrics—RMSE, precision@k, recall@k, MAP
- Scalability—using Apache Spark on AWS for large datasets
Why Recommender Systems?
- High business impact—increase engagement, retention, and revenue
- Interview favorite—frequently tested at FAANG and startups
- Blend of classic and deep learning—perfect for applied ML portfolios
Who Is This For?
- Machine learning engineers targeting e-commerce or media roles
- Data scientists wanting to expand beyond prediction
- Students building end-to-end applied ML systems
- Freelancers offering recommendation services
Your Gateway to Applied AI
Recommender systems sit at the sweet spot of **business value**, **technical depth**, and **real-world deployability**.
Ready to build what powers the digital economy? Enroll now.
