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Microcredential

Mathematics for Machine Learning

Imperial College London via Coursera Specialization

Overview

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. This specialization 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. 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.

Courses

Taught by

A. Freddie Page, David Dye, Marc Peter Deisenroth and Samuel J. Cooper

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Reviews

3.0 rating, based on 1 reviews

Start your review of Mathematics for Machine Learning

  • Profile image for Mateo Gómez
    Mateo Gómez
    Well, finally finished the three course specialization Mathematics for Machine Learning from Imperial College London and the feeling is a little bittersweet. The course content could not be more interesting, the teachers seem to be very good professionals...

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