Building Recommender Systems with Machine Learning and AI

Best Machine Learning Courses on Udemy

After completing the course, you will…

• Understand and apply user-based and item-based collaborative filtering to recommend items to users
• Create recommendations using deep learning at massive scale
• Build recommender systems with neural networks and Restricted Boltzmann Machines (RBM’s)
• Make session-based recommendations with recurrent neural networks and Gated Recurrent Units (GRU)
• Build a framework for testing and evaluating recommendation algorithms with Python
• Apply the right measurements of a recommender system’s success
• Build recommender systems with matrix factorization methods such as SVD and SVD++
• Apply real-world learnings from Netflix and YouTube to your own recommendation projects
• Combine many recommendation algorithms together in hybrid and ensemble approaches
• Use Apache Spark to compute recommendations at large scale on a cluster
• Use K-Nearest-Neighbors to recommend items to users
• Solve the “cold start” problem with content-based recommendations
• Understand solutions to common issues with large-scale recommender systems

Who this course is for:

• Software developers interested in applying machine learning and deep learning to product or content recommendations
• Engineers working at, or interested in working at large e-commerce or web companies
• Computer Scientists interested in the latest recommender system theory and research