Learn by Source
Imperial College London · on Coursera

Mathematics for Machine Learning Specialization

4.7(25,000) on Coursera·540K enrolled
Intermediate 120 hours English Specialization
SkillsLinear algebraMultivariable calculusPCAVectors and matricesOptimisation basicsMaths for ML

Is this course right for you?

Our take
A practical way to fill the maths gap that most ML courses assume, taught intuition-first rather than proof-heavy. It is free to audit.

Good for: learners who want the maths behind machine learning without a pure-maths course.

Skip if: you already have a strong maths background, or you want rigorous proofs.

Three courses from Imperial College cover the essentials — linear algebra, multivariate calculus (including the chain rule behind backpropagation), and PCA — implemented in Python so the maths connects to code. It is aimed at people who can follow an ML course but feel shaky on why the methods work.

It is not a rigorous pure-maths course, so it skips formal proofs, and someone with a strong maths background will not need it. Pair it with, or take it before, an ML course like Ng's. The certificate needs a subscription; the free audit covers the learning. The maths is foundational and still relevant.

Comparison · LBS

Compare alternatives for Mathematics for Machine Learning Specialization

Same topic, different options. We surface the trade-offs others hide so you can pick the course that actually fits your time, budget, and goals.
Coursera4.7(25,000)
Mathematics for Machine Learning Specialization
Price
Paid
Subscription-based, free to audit
Duration
120 hrs
Level
Intermediate
Certificate
Specialization
Coursera4.7(7,000)
AI in Healthcare Specialization
Price
Paid
Subscription-based, free to audit
Duration
120 hrs
Level
Intermediate
Certificate
Specialization
View course →
DataCamp4.6(5,000)
Machine Learning Scientist with Python Career Track
Price
Paid
DataCamp subscription
Duration
96 hrs
Level
Intermediate
Certificate
Specialization
View course →
Coursera4.5(35,000)
Applied Data Science with Python Specialization
Price
Paid
Subscription-based, free to audit
Duration
200 hrs
Level
Intermediate
Certificate
Specialization
View course →
Prices & availability can change — confirm on the provider's site. We're not affiliated with any single provider.

About this course

Mathematics for Machine Learning is the missing piece in most practitioners' ML education — it teaches the mathematics that underpins the algorithms, not just the algorithms themselves. Three courses cover linear algebra (vectors, matrices, eigenvalues — why matrix operations work the way they do), multivariate calculus (partial derivatives, the chain rule as the foundation of backpropagation, optimization), and PCA (dimensionality reduction from first mathematical principles). All three are taught by Imperial College London faculty, implemented in Python.

Browse all AI & ML courses Browse all Coursera courses

Instructor

MD
Marc Deisenroth / A. Aldo Faisal / Cheng Soon Ong
Coursera instructor
540K+ learners3 courses4.7 instructor rating

Taught by Marc Deisenroth, A. Aldo Faisal, and Cheng Soon Ong — Imperial College London faculty and co-authors of the textbook 'Mathematics for Machine Learning.'

Frequently asked questions

They run in sequence: linear algebra first, then multivariate calculus, and finally principal component analysis for dimensionality reduction. The aim throughout is intuition for the maths sitting underneath machine learning — vectors and matrices, gradients, and how algorithms 'find their way downhill' to a good answer — rather than abstract theorems for their own sake. By the end the maths in an ML course should feel less like magic.

Barely for the first two courses, but genuinely for the third. The principal component analysis course expects some Python and numpy, so if you have never coded, work through a short Python primer before you reach it. Otherwise you risk getting stuck on syntax rather than the actual maths, which would be a frustrating way to lose the thread near the finish.

It deliberately favours intuition over proofs, and some learners do find the courses short and easy as a result. That is a design choice, not a flaw: it aims to rebuild confidence and give you a working feel for the concepts, not to replace a university maths degree. If you want formal rigour and heavy problem sets, pair it with a proper textbook alongside.

Before or alongside works best. It explains why gradient descent, matrices, and covariance actually matter, so when you meet them in a course like Andrew Ng's they click instead of washing over you. Taking it afterwards still helps fill gaps, but you lose the early clarity that makes the first pass through an ML course far less bewildering.

Both are foundational primers, so the choice comes down to scope. This Imperial one concentrates on linear algebra, calculus, and PCA. The DeepLearning.AI version adds explicit probability and statistics and frames everything more around data science. If you want statistics folded in, lean toward that one; if you prefer the tighter, more established Imperial set focused on the core three areas, take this.

Yes, by auditing. Coursera lets you watch the lecture videos and read the materials at no cost, which for a course this explanation-led is genuinely most of the value. The graded assignments and the certificate need a paid subscription, with financial aid available if you apply. Auditing is a fine choice if you only want the understanding and can skip the marked quizzes.
Paid
Subscription-based, free to audit
Enroll now