This project-based course shows programmers of all skill levels how to use machine learning to build programs that can make recommendations—like recommending new products.
- [Adam] Recommendation systems are machine learning systems that help users discover new products and services. Every time you shop for a product online, recommendation systems are guiding you towards the products you are most likely to buy. Hi, I'm Adam Geitgey. I'm a software engineer, and I have a passion for helping developers learn to take advantage of the latest developments in machine learning. Even if you have no experience in machine learning, this course will teach you how to build a recommendation system from the ground up. In this course, we'll build a machine learning recommendation system using the same techniques that startups in Silicon Valley use.
Our system will be able to recommend new movies to users based on the reviews of movies they have already seen. But the exact same system can be used for any type of product or service that you want to recommend to a user. Recommendation systems are an essential feature in the modern world. Users are often overwhelmed by choice, and recommendation systems help them quickly find products they love. This leads to not only more sales, but also to happier users. You'll be able to take what you learned building the recommendation system in this course and apply it to your own projects. Let's get started.
Author
Adam Geitgey
Released
4/10/2017Recommendation systems are a key part of almost every modern consumer website. The systems help drive customer interaction and sales by helping customers discover products and services they might not ever find themselves. The course uses the free, open source tools Python 3.5, pandas, and numpy. By the end of the course, you'll be equipped to use machine learning yourself to solve recommendation problems. What you learn can then be directly applied to your own projects.
- Building a machine learning system
- Training a machine learning system
- Refining the accuracy of the machine learning system
- Evaluating the recommendations received
Skill Level Intermediate
Duration
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Introduction
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Welcome1m 1s
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Set up environment2m 15s
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1. The Basics of Making Recommendations
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2. Ways of Making Recommendations
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3. Getting to Know Our Tools
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4. Building the Framework for Our Recommendation System
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5. Collaborative Filtering with Matrix Factorization
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6. Testing Our System
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Use regularization1m 52s
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7. Using the Recommendation System in a Real World Program
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Find similar products1m 59s
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Conclusion
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Wrap up47s
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Video: Welcome