What I Learned from a Six-Session MBA Data Science Course — Building an Audit Assistant on Demo Day [Intro to ML #10]
In my previous post (Intro to ML #9), I covered ways to get more out of generative AI — prompts, RAG […]
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data science
Getting More Out of Generative AI — Prompts, RAG, and the Age of Agents [Intro to ML #9]
In my previous post (Intro to ML #8), I covered how generative AI actually works — the next-word pre […]
What’s Actually Inside a Generative AI? — From Language Models to Diffusion Models [Intro to ML #8]
In my previous post (Intro to ML #7), I covered the difference between traditional data analysis and […]
Analysis vs. AI Prediction: What’s Actually Different — and What Could Go Wrong [Intro to ML #7]
In my previous post (Intro to ML #6), I worked through the data exploration, feature engineering, an […]
Asking AI “Why Are You Quitting?” — My First Solo Machine Learning Challenge [Intro to ML #6]
In my previous post (Intro to ML #5), I covered regression — using machine learning to predict conti […]
Predicting “How Much?” — Machine Learning Beyond Classification [Intro to ML #5]
In my previous post (Intro to ML #4), I wrote about thresholds, the recall-precision tradeoff, and w […]
The Threshold Decision: How Machine Learning Actually Changes Business Outcomes [Intro to ML #4]
In my previous post (Intro to ML #3), I covered confusion matrices, AUC, and why accuracy alone is a […]
Why a 99% Accurate Model Can Be Completely Useless [Intro to ML #3]
In my previous post (Intro to ML #2), I wrote about the unglamorous reality of data prep — missing v […]
Machine Learning: It’s 90% Data Prep Before You Even Build a Model [Intro to ML #2]
In my previous post (Intro to ML #1), I wrote about the difference between AI, machine learning, and […]