Building Recommender Systems with Machine Learning and AI
With Frank Kane
Liked by 1,596 users
Duration: 9h 5m
Skill level: Beginner Intermediate
Released: 4/12/2019
Course details
Automated recommendations are everywhere: Netflix, Amazon, YouTube, and more. Recommender systems learn about your unique interests and show the products or content they think you’ll like best. Discover how to build your own recommender systems from one of the pioneers in the field. Frank Kane spent over nine years at Amazon, where he led the development of many of the company’s personalized product recommendation technologies. In this course, he covers recommendation algorithms based on neighborhood-based collaborative filtering and more modern techniques, including matrix factorization and even deep learning with artificial neural networks. Along the way, you can learn from Frank's extensive industry experience and understand the real-world challenges of applying these algorithms at a large scale with real-world data. You can also go hands-on, developing your own framework to test algorithms and building your own neural networks using technologies like Amazon DSSTNE, AWS SageMaker, and TensorFlow.
Skills you’ll gain
Earn a sharable certificate
Share what you’ve learned, and be a standout professional in your desired industry with a certificate showcasing your knowledge gained from the course.
LinkedIn Learning
Certificate of Completion
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Showcase on your LinkedIn profile under “Licenses and Certificate” section
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Download or print out as PDF to share with others
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Meet the instructor
Learner reviews
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Neha Jhamb
Neha Jhamb
Data Scientist | M.Tech in Machine Learning - IIITD
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Jonathan McCulloch
Jonathan McCulloch
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Amjad Al-Blewi
Amjad Al-Blewi
Manager -Transmission Asset Management (OHL) in Dubai Electricity and Water Authority
Contents
What’s included
- Practice while you learn 1 exercise file
- Learn on the go Access on tablet and phone