Building Recommender Systems with Machine Learning and AI

Preview this course

Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you would 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.

Unlimited access to 750+ courses.
Enjoy a Free Trial. Cancel Anytime.

- OR -

30-Day Money-Back Guarantee
Full Lifetime Access.
130 on-demand videos & exercises
Level: Beginner
11hrs 24mins
Access on mobile, web and TV

What to know about this course

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. 

Who's this course for?

This course is suitable for software developers, engineers, and computer scientists who are looking to build recommender systems using the principles of machine learning, deep learning, and artificial intelligence (AI).

A basic understanding of Python programming and algorithms is needed to get started with this course.

What you'll learn

  • 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.

Key Features

  • Learn how to build recommender systems using various methods and algorithms
  • Apply real-world learnings from Netflix and YouTube to your recommendation projects
  • A comprehensive, hands-on, and filled with practical coding exercises to leverage your learnings.

Course Curriculum

About the Author

Frank Kane

Frank Kane has spent nine years at Amazon and IMDb, developing and managing the technology that automatically delivers product and movie recommendations to hundreds of millions of customers all the time. He holds 17 issued patents in the fields of distributed computing, data mining, and machine learning. In 2012, Frank left to start his own successful company, Sundog Software, which focuses on virtual reality environment technology and teaches others about big data analysis.

40% OFF! Unlimited Access to 750+ Courses. Redeem Now.