AB Innovate

Learnings

A designer's journal through machine learning — written for visual thinkers.

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Breaking down the fundamentals of machine learning — from AI to deep learning — in a way that makes sense if you think visually.

A quick-start guide to Python data structures and libraries you'll use constantly in ML — tuples, dictionaries, sets, and essential imports.

Before you build any model, you need to know your data inside out. Here's how EDA helps you ask the right questions.

A visual guide to the charts and plots used in ML — from histograms to violin plots — and what each one reveals about your data.

Your first ML model — understanding how linear regression draws the best-fit line through your data, and how to know if it's any good.

When the answer is yes or no — understanding classification, the sigmoid curve, and how to measure if your model is actually right.

Reproducibility, virtual environments, and the habits that separate a quick experiment from production-ready machine learning.