This course will teach you how to use Python, artificial intelligence (AI), machine learning, and deep learning to build a recommender system. From creating a simple recommendation engine to building hybrid ensemble recommenders, you will learn key concepts effectively and in a real-world context.
The course starts with an introduction to the recommender system and Python. Learn how to evaluate recommender systems and explore the architecture of the recommender engine framework. Next, you will learn to understand how content-based recommendations work and get to grips with neighborhood-based collaborative filtering. Moving along, you will learn to grasp model-based methods used in recommendations, such as matrix factorization and Singular Value Decomposition (SVD).
Next, you will learn to apply deep learning, artificial intelligence (AI), and artificial neural networks to recommendations and learn how to scale massive datasets with Apache Spark machine learning. Later, you will encounter real-world challenges of recommender systems and learn how to solve them. Finally, you will study the recommendation system of YouTube and Netflix and find out what a hybrid recommender is.
By the end of this course, you will be able to build real-world recommendation systems that will help users discover new products and content online.
All the resource files are added to the GitHub repository at:
https://github.com/packtpublishing/building-recommender-systems-with-ma…
Get a basic overview of the architecture of recommender systems
Test and evaluate recommendation algorithms with Python
Use K-Nearest-Neighbors to recommend items to users
Find solutions to common issues with large-scale recommender systems
Make session-based recommendations with recurrent neural networks
Use Apache Spark to compute recommendations at a large scale on a cluster
You will learn with the help of real-world case studies, activities, and coding exercises throughout this course’s journey.