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

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.

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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.

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What's inside

Three courses

Mathematics for Machine Learning: Linear Algebra

(0 hours)
In this course on Linear Algebra, we examine its relationship to vectors and matrices. We explore vectors and matrices, including eigenvalues and eigenvectors, and their applications in data-driven scenarios. We implement these concepts in code, using Python and Jupyter notebooks. By the end, you'll have an intuitive understanding of linear algebra and its relevance to machine learning.

Mathematics for Machine Learning: Multivariate Calculus

(0 hours)
This course provides an introduction to the multivariate calculus needed for machine learning. We start with the basics of calculus, including the definition of the gradient. We then learn how to calculate vectors that point uphill on multidimensional surfaces and use this to build approximations to functions. We also discuss how calculus is used in the training of neural networks and linear regression models.

Mathematics for Machine Learning: PCA

(0 hours)
This intermediate-level course introduces the mathematical foundations of Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover basic statistics, compute distances and angles between vectors, and derive orthogonal projections of data. Using these tools, we'll derive PCA as a method that minimizes the average squared reconstruction error.

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