Imperial College London · on Coursera

Mathematics for Machine Learning Specialization

4.7(25,000) on Coursera·540K enrolled
Intermediate 120 hours English Specialization
Our recommendation
Imperial College's specialisation covering the maths that machine learning relies on — linear algebra, multivariable calculus and PCA. It is applied and intuition-led rather than proof-heavy, aimed at filling the maths gap before or alongside an ML course.

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

Less suitable if: You already have a strong maths background, or you want rigorous proofs.

Skills you'll gain

Linear algebraMultivariable calculusPCAVectors and matricesOptimisation basicsMaths for ML

Is this course right for you?

A good fit if you…

You want the maths for ML
You prefer intuition over proofs
You have school-level maths

Consider something else if you…

You already have strong maths
You want rigorous proofs
You want to skip straight to models

Requirements: School-level maths; some Python for the exercises.

Realistic time: Around 120 hours across three courses; two to three months part-time.

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.

What you'll learn

Apply linear algebra operations and understand their geometric interpretation
Compute partial derivatives and understand gradient descent from first principles
Understand eigenvectors and eigenvalues and their role in ML algorithms
Implement PCA from scratch using the mathematical foundations
Read and interpret mathematical notation in ML research papers

This course includes

120h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language

What it costs

Free to audit on Coursera; the certificate needs a subscription, with financial aid available.

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Is the certificate recognised?

The certificate is issued through Coursera with Imperial College London. The understanding you gain matters more than the certificate itself.

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.'

About this provider

CO
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Frequently asked questions

Yes if the maths in ML courses trips you up. It focuses on the linear algebra, calculus and PCA you actually need, with intuition over proofs.
No. It builds from school-level maths, though a willingness to practise helps.
Either works. Many take it alongside or just before an ML course to fill the maths gap.
You can audit it free on Coursera; the certificate needs a subscription, with financial aid available.
It is intermediate and takes effort, but it is applied rather than proof-heavy, which makes it manageable.
Some exercises use Python, so a little familiarity helps.
Paid
Subscription-based, free to audit
Enroll now