Books on the maths behind machine learning.

↓ no boring textbooks here

Two series that teach you everything from linear algebra to supervised learning — through heist stories, bee colonies, and zero gatekeeping.

Trusted by 10,000+ readers worldwide

Linear algebraCalculusProbability & statsSupervised learning

Before Machine Learning

— the foundations

start here if you're building from zero ↓

Linear algebra
A space odyssey through vectors ✦
$9.99

Vol 1Linear algebra

Build a movie recommendation system from scratch, one vector at a time.

Calculus
Starring 47 confused bees 🐝
$9.99

Vol 2Calculus

A bee colony optimises its hive and teaches you derivatives along the way.

Probability & stats
A €100M diamond heist 💎
$9.99

Vol 3Probability & stats

Crack the vault of the Antwerp Diamond Centre using Bayes' theorem and a bit of nerve.

All 3 foundations → $24.99

Save $5 vs buying individually. save 30% paying in ₿ — just $17.49

Now Machine Learning

— from theory to codeNew

you know the maths, now build the models ↓

Standard EditionSupervised Learning — Limited Edition
$9.99

Vol 1Supervised learning

Join me at the data science team of a mythical insurance company that underwrites yetis, dragons, and everything in between.

Two editions, same book

Complete library (4 books) → $34.99

Save $5 vs buying individually. save 30% paying in ₿ — just $24.49

The reading path

Linear algebra

Vectors & matrices

Calculus

Derivatives & integrals

Probability

Distributions & Bayes

Supervised learning

← you are here

Unsupervised learning

soon

What readers say

I wish I found this 4 weeks ago

I have been SO frustrated trying to learn linear algebra. This book has been a breath of fresh air.

— John C.

Readable like a novel

You understand not just how it works, but exactly WHY it works.

— Mason

All-time best series

Teaching probability through a heist story is genius. Every chapter left me wanting more.

— Cleric

Heist plot makes it easy

The diamond heist kept me turning pages. Didn't realise I was learning Bayes' theorem until it clicked.

— Andre

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