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Building Recommender Systems with Machine Learning and AI you own this product

Frank Kane
  • Course duration: 9h 13m
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You've seen automated recommendations everywhere—on Netflix's home page, on YouTube, and on Amazon. Now build your own recommendation systems to help people discover new products and content, using deep learning, neural networks, and machine learning. In Building Recommender Systems with Machine Learning and AI, you’ll learn from Frank Kane, who led the development of many of Amazon's recommendation technologies, and unlock one of the most valuable applications of machine learning today.


Distributed by Manning Publications

This course was created independently by big data expert Frank Kane and is distributed by Manning through our exclusive liveVideo platform.

about the subject

You've seen automated recommendations everywhere—on Netflix's home page, on YouTube, and on Amazon. To accomplish this, 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 both prestigious tech employers and enterprises of all sizes, and by understanding how they work, you'll become very valuable to them.

about the video

Learn how to build recommender systems from Frank Kane, one of Amazon's pioneers in the field of ML-based recommender systems. In Building Recommender Systems with Machine Learning and AI, you’ll cover tried and true recommendation algorithms based on neighborhood-based collaborative filtering, and work your way up to more modern techniques such as matrix factorization and even deep learning with artificial neural networks. As you go, you’ll develop your own framework for evaluating and combining many different recommendation algorithms together and build your own neural networks using Tensorflow to generate recommendations from movie ratings data. Along the way, you'll learn from Frank's extensive industry experience to understand the challenges you'll encounter when applying these algorithms at large scale and with real-world data.

prerequisites

For experienced software developers or computer scientists.

what you will learn

  • Building a recommendation engine
  • Content-based filtering using item attributes
  • Neighborhood-based collaborative filtering with user-based, item-based, and KNN CF
  • Model-based methods including matrix factorization and SVD
  • Applying deep learning, AI, and artificial neural networks to recommendations
  • Case studies from YouTube and Netflix

about the instructor

Frank Kane holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. He spent 9 years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to millions of customers every day. Sundog Software, his own company specializing in virtual reality environment technology and teaching others about big data analysis, is his pride and joy.
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