Mathematics & Statistics for Machine Learning
Demystify the mathematics that ML rests on — linear algebra, calculus, probability and statistics — taught visually and computationally rather than by proof. Learners connect each concept directly to where it appears inside a model, from gradients in backpropagation to distributions in evaluation.
What this course covers.
5 modules, 34 named skill atoms. Expand any module to see them.
1Linear Algebra for ML7 skill atoms
2Calculus of Learning7 skill atoms
3Probability Essentials7 skill atoms
4Statistics for Evaluation7 skill atoms
5Applied Notebook Lab6 skill atoms
AI-103 in the role journeys.
This course appears in 2 of our 45 role journeys. Here is what a learner takes immediately before and after it in each.
Roles this course serves
This course is authored to band B2.
Every course we run is written to one rung of the CASI ladder, so a plan can be assembled to take a team from where they are to where they need to be.
What do B1–B6 mean?The CASI Capability Ladder — click to expand
Every course targets a band on the CASI Capability Ladder — our six-band proficiency scale, anchored to open standards (O*NET, ESCO, NICE, NIST AI RMF, Bloom's). A band tells you how deep a course goes, and what evidence proves it.
A note on B6. Courses in this catalog target B1–B5. B6 is not taught — it is recognised, through a portfolio and a panel, once someone is setting direction for others. Every journey here is built to land a learner at B5.
Other AI courses at this level.
Python for AI Engineering
Data Wrangling & EDA with pandas
SQL & Data Foundations for AI Teams
Prompt Engineering Essentials
Generative AI Foundations
Visualization & Data Storytelling
Run AI-103 for your team.
This course runs at several lengths depending on how deep you need to go and how much of it your people already have. Tell us who is being trained and we will scope it.