Building Recommender Systems with Machine Learning and AI

Get started with building intelligent recommender systems

About This Video

• Learn how to build recommender systems using various methods and algorithms
• Apply real-world learnings from Netflix and YouTube to your recommendation project
• Combine many recommendation algorithms in hybrid and ensemble approaches

In Detail

Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you like to watch? Are you looking for a practical course that will teach you how to build intelligent recommendation systems? This course will show you how to build accurate recommendation systems in Python using real-world examples.

The course starts with an introduction to the recommender system and Python. You will then 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 data sets 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 is a hybrid recommender.

By the end of this course, you will be able to build real-world recommendation systems that will help the users to discover new products and content online.