If you strip machine learning of all the complex terminology and buzzwords, you almost always end up with the same core idea: we represent real-world objects as numbers and work with those numbers mathematically. This is where vectors, dimensions, and feature spaces come in. As PHP developers, it’s especially important to understand this intuitively rather than formally, because in code you’ll deal not with abstract linear algebra, but with arrays of numbers, matrices, and operations on them. A vector as a way to describe an object In machine learning, a vector is simply an ordered set of numbers. Each number represents some aspect of an object. If the object is simple, the vector is short. If the object is complex, the vector can be very long. Imagine a user of an online store. We can describe them using: age, number of purchases per year, and average order value.…