Math and Statistics for AI Engineers
Learn just enough math for AI engineering: vectors, matrices, gradients, loss curves, and the statistics behind every evaluation metric you will read.
What You'll Learn
Understanding Why This Module Is Deliberately Small
The trap most beginners fall into acme-shop's first AI engineer trained a model on the clean order data from Data Handling and SQL for AI Engineers.
Linear Algebra - the Language Models Are Written In
Linear algebra is the maths of lists and grids of numbers, and it is the language every neural network layer is written in.
Calculus - Just Enough to Understand Training
You need three calculus ideas: a gradient tells you which way to nudge a weight, gradient descent repeats that nudge, and the chain rule is how the...
Probability and Statistics - Handling Uncertainty
Models output probabilities, not certainties, and every evaluation metric you read is a statistic.
Hands-on Lab
You will compute statistics on simulated acme-shop order values, watch gradient descent converge and then diverge, diagnose three loss curves, and...
Quick Reference
Concepts at a glance Which check to run first When something looks wrong, check these in order. They take minutes and rule out the common causes.
Skills You'll Master
Curriculum Index7 topics
Understanding Why This Module Is Deliberately Small
The trap most beginners fall into acme-shop's first AI engineer trained a model on the clean order data from Data...
Linear Algebra - the Language Models Are Written In
Linear algebra is the maths of lists and grids of numbers, and it is the language every neural network layer is written...
Calculus - Just Enough to Understand Training
You need three calculus ideas: a gradient tells you which way to nudge a weight, gradient descent repeats that nudge...
Probability and Statistics - Handling Uncertainty
Models output probabilities, not certainties, and every evaluation metric you read is a statistic.
Hands-on Lab
You will compute statistics on simulated acme-shop order values, watch gradient descent converge and then diverge...
Quick Reference
Concepts at a glance Which check to run first When something looks wrong, check these in order.
Common Mistakes
Mistakes with training and shapes Blaming the architecture when the learning rate is the real problem is the classic...
Career Impact
Roles that use the skills in this module.
AI Engineer
MLOps Engineer
Platform Engineer
Next Modules
Related Guides
Practice on the Coding Sheet
Not a software engineer sheet. Every problem comes from real DevOps, SRE, Platform and Cloud interviews, from your first script to a system you build yourself.
Open the Coding SheetFrequently Asked Questions
Far less than a maths degree. You need vectors and matrices, the idea of a gradient, how to read a loss curve, and basic statistics for judging data and metrics. Frameworks handle the calculations for you.
No. PyTorch computes every gradient automatically. You only need the idea, so you can reason about why a loss curve looks wrong.
The most common cause is a learning rate that is too high, so each step overshoots the minimum. Lower it by about 10 times and rerun before you suspect the architecture.
It measures how closely two vectors point in the same direction, which makes it the standard way to compare embeddings. Semantic search and RAG systems use it to find text with similar meaning.