[Packtpub] Building Recommender Systems with Machine Learning and AI

Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.

Video Description

Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon’s personalized product recommendation technologies. You’ve seen automated recommendations everywhere—on Netflix’s home page, on YouTube, and on Amazon–as these machine learning algorithms learn about your unique interests, and show the best products or content for you as an individual. These technologies have become central to the largest, most prestigious tech employers out there, and by understanding how they work, you’ll become very valuable to them. We cover tried-and-true recommendation algorithms based on neighborhood-based collaborative filtering and work our way up to more modern techniques including matrix factorization and even deep learning with artificial neural networks. Along the way, you’ll learn from Kane’s extensive industry experience and understand the real-world challenges you’ll encounter when applying these algorithms at a large scale and with real-world data. The coding exercises in this course use the Python programming language. We include an intro to Python if you’re new to it, but you’ll need some prior programming experience in order to use this course successfully. We also include a short introduction to deep learning if you are new to the field of artificial intelligence, but you’ll need to be able to understand new computer algorithms. Hope to see you in the course soon!

Style and Approach

This course is very hands-on; you’ll develop your framework for evaluating and combining many different recommendation algorithms together, and you’ll even build your own neural networks using TensorFlow to generate recommendations from real-world movie ratings, from real people.

What You Will Learn

• 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 insights 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 on a large scale on a cluster
• Use K-Nearest-Neighbours to recommend items to users
• Solve the “cold start” problem with content-based recommendations
• Understand solutions to common issues with large-scale recommender systems

Created By Frank Kane
Released Date September 21/2018
Duration 9h 14m

Size: 2.89 GB

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https://www.packtpub.com/application-development/building-recommender-systems-machine-learning-and-ai-video.