Think in vectors: How to work with large data sets efficiently

- [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…