The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting. The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data.
Then we look through what vectors and matrices are and how to work with them. In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. 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. 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.
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