Building Recommender Systems with Machine Learning and AI [Video]
English | Size: 2.92 GB
Category: CBTs
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.
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