BUILDING RECOMMENDER SYSTEMS WITH MACHINE LEARNING AND AI
Help people discover new products and content with deep learning, neural networks, and machine learning recommendations.
BestsellerCreated by Sundog Education by Frank Kane, Frank KaneLast updated 9/2018 EnglishWhat you’ll learn
This course will teach you how to:
- Understand and apply user-based and item-based collaborative filtering to recommend items to users
- Create recommendations using deep learning at a massive scale
- Build recommender systems using neural networks and Restricted Boltzmann Machines (RBM’s)
- Make session-based recommendations using recurrent neural networks and Gated Recurrent Units (GRU)
- Build a framework for testing and evaluating recommendation algorithms using Python
- Measure the success of a recommender system using appropriate metrics
- Build recommender systems using matrix factorization methods such as SVD and SVD++
- Apply real-world examples from Netflix and YouTube to your own recommendation projects
- Use hybrid and ensemble approaches to combine multiple recommendation algorithms
- 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
The course requires a Windows, Mac, or Linux PC with at least 3GB of free disk space, some experience with a programming or scripting language (preferably Python), and some computer science background.
Learn how to build recommender systems from one of Amazon’s pioneers in the field. Frank Kane spent over nine years at Amazon, where he managed and led the development of many of Amazon’s personalized product recommendation technologies.
Are you interested in learning about the technology behind automated recommendations on platforms like Netflix, YouTube, and Amazon? If so, this course is for you. It covers the most widely used recommendation algorithms, including neighborhood-based collaborative filtering, matrix factorization, and even deep learning with artificial neural networks. With the guidance of Frank’s extensive industry experience, you’ll learn how to apply these algorithms at large scale and with real-world data.
Please note that this course is not a “learn-to-code” format. We assume that you already know how to code and that you have a good understanding of computer algorithms. Instead, the course focuses on understanding the different algorithms and how to choose when to apply each one for a given situation. You will develop your own framework for evaluating and combining many different recommendation algorithms together, and you will even build your own neural networks using Tensorflow to generate recommendations from real-world movie ratings from real people.
The coding exercises in this course use the Python programming language. If you’re new to Python, this course includes an introduction to the language. However, it’s important to note that prior programming experience is required to fully utilize the course. Additionally, while a short introduction to deep learning is provided for those new to the field of AI, a basic understanding of computer algorithms is necessary to fully understand the material.
High-quality, hand-edited English closed captions are included to help you follow along.
I hope to see you in the course soon!Who is the target audience?
- Software developers interested in applying machine learning and deep learning to product or content recommendations
- Engineers working at, or interested in working at large e-commerce or web companies
- Computer Scientists interested in the latest recommender system theory and research