Mathematics for Machine Learning, Short Course | Part time online | Coursera | United States
4 months
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This Mathematics for Machine Learning Specialization offered by Coursera in partnership with Imperial College London aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

Visit the official programme website for more information


For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. 

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.

At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

Applied Learning Project

Through the assignments of this Mathematics for Machine Learning Specialization offered by Coursera in partnership with Imperial College London, you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.

Programme Structure

Courses include:

  • Mathematics for Machine Learning: Linear Algebra
  • Mathematics for Machine Learning: Multivariate Calculus
  • Mathematics for Machine Learning: PCA

Key information


  • Part-time
    • 4 months
    • 4 hrs/week

Start dates & application deadlines

You can apply for and start this programme anytime.




  • Self-paced

Academic requirements

We are not aware of any specific GRE, GMAT or GPA grading score requirements for this programme.

English requirements

We are not aware of any English requirements for this programme.

Other requirements

General requirements

Beginner Level

  • No prior experience required.

Tuition Fee

To always see correct tuition fees
  • International

    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 4 months.
  • National

    Tuition Fee
    Based on the tuition of 0 USD for the full programme during 4 months.

You can choose from hundreds of free courses, or get a degree or certificate at a breakthrough price. You can now select Coursera Plus, an annual subscription that provides unlimited access. 


Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project.

Studyportals Tip: Students can search online for independent or external scholarships that can help fund their studies. Check the scholarships to see whether you are eligible to apply. Many scholarships are either merit-based or needs-based.

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Mathematics for Machine Learning


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