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Mental Models
Machine Learning for Non-Technical Professionals
Why Your “Silly Questions” Are Product Requirements We have a technical writer on our team. She’s sharp, curious, and exactly the...
A Friendly Introduction to Principal Component Analysis
Most datasets don’t have two or three measurements per observation. They have dozens, sometimes hundreds. A patient record might include blood...
Bias vs. Variance: Why Your ML Model Can’t Have It All
For years, every time I fixed one problem with my models, I created another one. Make the model more sophisticated to...
Training vs Testing Data: ML Models Must Prove Themselves
Does this sound familiar? You’re tutoring a student for an upcoming math test. You help them solve dozens of practice problems...
Inference vs. Prediction: Different Goals in ML Analysis
Some machine learning applications can make accurate recommendations but can’t explain the reasoning. Others provide clear explanations but aren’t quite as...
Classification vs Regression: Predicting What vs. How Much
In our previous post, we explored supervised vs. unsupervised learning. Now we’re diving into another fundamental choice you’ll face in every...
Supervised vs Unsupervised Learning: The Two Main Ways Machines “Learn”
Remember learning to ride a bike? Some of us had a parent running alongside, holding the seat and shouting guidance: “Pedal...
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