221. The k-nearest neighbors algorithm

Learn k-NN by using it in classification, regression, and interpolation case studies

k-nearest neighbors is one of the most versatile and robust machine learning algorithms there is. In this course we'll walk through its implementation in Python (just a few lines of code), and how to use it to perform classification, regression, and interpolation. We'll apply it to several different types of data sets, too, and use it to identify penguins, price diamonds, and interpolate spatial data, like we would need to do if mapping ocean temperatures with automated drones.

At this point, the course is still a work in progress, but it's open for registration. If you'd like to preview the material, the first portion of the course is freely available in the lesson list below. Prerequisites are the basics of Python -- like for loops, functions and f-strings -- or at least the willingness to learn them. We'll walk through all the code line by line, so you should be able to fill in any gaps you have as we go.

And if you're interested in the other End to End Machine Learning course offerings, keep in mind that you can get access to the entire course catalog for just $9 USD per month or $99 USD for all time.

Frequently Asked Questions

When does the course start and finish?
The course starts now and never ends! It is a completely self-paced online course - you decide when you start and when you finish.
How long do I have access to the course?
How does lifetime access sound? After enrolling, you have unlimited access to this course for as long as you like - across any and all devices you own.
What if I am unhappy with the course?
We would never want you to be unhappy! If you are unsatisfied with your purchase, contact us in the first 30 days and we will give you a full refund.

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Your Instructor

Brandon Rohrer
Brandon Rohrer

I love solving puzzles and building things. Machine learning lets me do both. I got started by studying robotics and human rehabilitation at MIT (MS '99, PhD '02), moved on to machine vision and machine learning at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, and cloud data science at Microsoft. At Facebook I worked to get internet and electrical power to those in the world who don't have it, using deep learning and satellite imagery and to do a better job identifying topics reliably in unstructured text. Now at iRobot I work to help robots get better and better at doing their jobs. In my spare time I like to rock climb, write robot learning algorithms, and go on walks with my wife and our dog, Reign of Terror.