K-Means from Scratch
Cluster data into natural groups by implementing k-means yourself: assign, recompute, repeat. The workhorse of unsupervised machine learning.
What you'll be able to build
Cluster data into natural groups by implementing k-means yourself: assign, recompute, repeat. The workhorse of unsupervised machine learning. Along the way you pick up real, transferable R skills, not just this one project:
- euclidean distance between points
- assigning points to the nearest centroid
- recomputing centroids as group means
- iterating until convergence
- vectorised distance computation
- evaluating cluster quality
A course like this one
Yours is built from your own placement, so module count and depth will differ. This map shows what a advanced-level R learner building K-Means from Scratch actually gets.
- Module 1: Coercion edge cases and the integer/double divide5 lessons
Builds the production-ready version of the vector for your k-means from scratch.
- Module 2: Closures, Reduce accumulate, and functional pipelines5 lessons
Builds the production-ready version of the reusable factor for your k-means from scratch.
- Module 3: Monte Carlo simulation and bootstrap from base R5 lessons
Builds the production-ready version of the simulation for your k-means from scratch.
- Module 4: Tidy reshaping and factor-level engineering5 lessons
Builds the production-ready version of the apply pipeline workflow for your k-means from scratch.
- Module 5: Recycling, NextMethod, and S3 dispatch surprises5 lessons
Builds the production-ready version of the data frame that powers your k-means from scratch.
- Module 6: Reproducible, NA-safe, self-checking analyses3 lessons
Builds the production-ready version of the summary table for your k-means from scratch.
How the lessons actually work
Every lesson has you predict what a piece of R code will output before you run it, then run it for real in your browser and fix what you got wrong. Each module ends in a challenge gate with hidden tests, so you can't advance until your code actually works. The course closes with a capstone that assembles everything into K-Means from Scratch, and a runnable proof page tied to your own code.
Common questions
How long does the K-Means from Scratch course take?
about 7 hours, across 6 modules and 28 lessons, at roughly 15 minutes per lesson. Your own course may run shorter or longer, since it's sized to your placement result, not a fixed template.
Do I need experience?
Yes. This is an advanced-tier R project, so it assumes you're already comfortable writing and reading R before you start.
How much does it cost?
$15 one-time, no subscription. The first module is free, so you can see exactly how the course teaches before you pay for the rest.