Building Recommendation Systems with Machine Learning and AI [ UDEMY ]

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Building Recommendation Systems with Machine Learning and AI

In this course you will Master :

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

Prerequisites and Requirements :

• Windows, Mac, or Linux system with at least 3GB of free disk space.
• Basic understanding with a programming or scripting language (preferably Python)
• Basic computer science background, and an ability to understand new algorithms.

This course is Intended for :

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

Size: 4.47G