Building Recommender Systems with Machine Learning and AI

Course Overview

recommender systems with Python and Apache Spark, and you'll learn how to evaluate and optimize them. In this course, participants will learn how to create machine learning recommendation systems with deep learning, collaborative filtering, and Python. Through hands-on activities, participants 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 recommendation engines 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 their 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, and use Tensorflow Recommenders (TFRS) and Generative Adversarial Networks for recommendations (GANs). Participants will also learn from Frank Kane, Amazon's pioneer in the field, who spent over nine years at Amazon, managing and leading the development of many of Amazon's personalized product recommendation systems. This course is not a learn-to-code type of format; participants should already know how to code. However, it is very hands-on; participants will develop recommender systems with Python and Apache Spark, and learn how to evaluate and optimize them. By understanding how these technologies work, participants will become very valuable to the largest, most prestigious tech employers out there.