You know how there are lots of blog posts out there about machine learning algorithms "in plain English"? They're popular because many people want to learn about machine learning, but don't want to be bombarded with heavy maths the moment they start. I agree, I think machine learning should be taught intuition first. I also recognise that people are busy and don't want to spend hours reading up on algorithms to understand them.
So forget "machine learning in plain English". Instead, I present some of the most popular algorithms in haiku form. Consider it "machine learning for the busy".
Logistic Regression
Inputs weighted, summed.
Passed through logistic function.
Shall we output 1?
Random Forest
Oh, decision trees!
Alone you're not so useful.
How 'bout a forest?
Support Vector Machines
Binary outcomes.
Maximum separation,
Is what is needed.
Linear Regression
Weighted X is summed.
Perhaps regularised, too.
Gives me real numbers.
Neural Networks
Backpropagation:
Works well, is a mystery.
But Geoff Hinton knows.
K Nearest Neighbours
Nothing to learn here.
Find the nearest data points,
And use the average.
K-means Clustering
Find similar things
By grouping based on distance
There's no "right" answer.
Markov Chains
Mr Markov, your
Use of probabilities
Gave us funny text.
Naive Bayes
We're gonna pretend
Features are independent,
Naive as that is.
About David
I'm a freelance data scientist consultant and educator with an MSc. in Data Science and a background in software and web development. My previous roles have been a range of data science, software development, team management and software architecting jobs.
