Learn how to store all product recommendations in a vector for efficiency.
- [Instructor] In machine learning,…we often work with large arrays of data.…These arrays are sometimes called vectors…for single columns of data…and matrices for larger arrays…because of the linear algebra roots of machine learning.…Let's look at how to work with vectors in code.…Let's open up vectors pt1.py.…Here we have a simple array or vector…representing all the ratings…that a single movie received from different users.…When we are using machine learning algorithms,…we'll often need to apply the same mathematical operation…to an entire array.…
Let's say we want to convert all these five star ratings…to a 10 point scale.…In other words, we want to multiply each rating by two.…What's the fastest way to do this?…In traditional programming, the standard solution is…to loop through the array one row at a time…using a for loop.…Let's run the code and check the output.…We'll choose right-click and Run.…You can see we made 12 separate updates…to the array before we got the final result.…This works, but doing multiplication on each element…
Author
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: Think in vectors: How to work with large data sets efficiently