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        Your Nearest Neighbor: The Elegant Mathematics of the Nearest Neighbors Algorithm

        Your Nearest Neighbor: The Elegant Mathematics of the Nearest Neighbors Algorithm

        "I want to get into machine learning, but there is no way I would understand the math part."

        Are you intimidated by the math that keeps coming up in machine learning? The field is undeniably attractive: The algorithms and applications are awesome and the future of the field looks bright. You'd love to get into it. But you‘re afraid you just can't keep up with the mathematical side of things. When you try, you just hit a brick wall of linear algebra, probability theory, statistics, and calculus. Does it really have to be so hard?

        "It feels like machine learning literature is deliberately obfuscated to keep out those who are afraid to wade through the math."

        As you think about the prospect of learning all that math, maybe you tell yourself that your math is rusty and maybe you even start to think that this field isn't for you after all. Even if you have some mathematical skills, it looks like an uphill battle. You can stand only so many unmotivated, out-of-nowhere equations.

        "I wish there were a course that teaches math in the context of solving some relevant problem."

        Learning mathematics for its own sake, unconnected to practical applications, works for some, but it isn‘t for everyone. You‘re willing to put in an hour or two here and there. You want to take the math seriously, but in the face of a long list of mathematical prerequisites...you just can‘t get into it.

        What if you could see the math behind machine learning?

        What if you knew your mathematical bases were covered? The doors to this exciting field would be wide open for you. You decide how far you want to go.

        ML math? No big deal.

        It's true that ML can, at times, be math-heavy. But you don't need to be a mathematician to get started. Despite appearances, much of the math that comes up in machine learning doesn‘t go beyond the high school level.

        How awesome would it feel to have ML math firmly in grip?

        Once you can see the math behind ML you‘ll be able to think clearly about how it works. There would be no more “magic”. When it comes time for you to adapt a machine learning algorithm to your special case, you‘ll be on firm grounding. When that model you just built isn't performing as well as you expect, you‘ll be able to pinpoint the reason.

        Good news: You can master the math behind ML quickly

        Learn how you can get started with ML math with my mini-course, Your Nearest Neighbor.

        Rather than cover a wide range of methods (regression, support vector machines, neural networks, etc. etc.), you‘ll get started in ML by learning just one technique—the nearest neighbors algorithm—and see exactly what mathematics underlie it.

        Nearest neighbors is a simple but powerful technique used throughout machine learning and in applications such as recommendation systems. Rather than starting with a bunch of mathematics that you may or may not know, you'll learn the needed math in context: the course works backwards from the algorithm and gradually uncovers, step by step, the math that really goes into this one technique. With my course you can unlock the doors (well, one door, anyway) to this fascinating field. Get up to speed—in days, not months or years (!)—with the math you need.

        Strip away complicated mathematics to see what makes ML tick

        When you‘ve reached the end of the course, you‘ll see the geometry that underlies the nearest neighbors algorithm in machine learning. Along the way, you'll be taking your first steps in linear algebra. By concentrating on just this one algorithm, there is no need to get lost in the weeds trying to learn all of linear algebra out-of-context, unconnected to machine learning.

        You‘ve got this

        Once you‘ve worked through the math behind the nearest neighbors algorithm, you‘ll have the confidence that you know what it takes to master the math behind an ML algorithm. You'll be in a strong position to move on to other parts of machine learning. Step into machine learning with confidence that you've got the math firmly in grip.

        About your author

        I'm Jesse Alama.  I did my Ph.D. in philosophy of mathematics at Stanford University and now work as a full stack data scientist in the private sector.  I love to teach mathematics.  Whether you're looking to switch careers and break into the field or are still in high school, my aim with Your Nearest Neighbor is to help you to break through the fog of uncertainty and step into the field with confidence.

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