This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction.Read more.
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The techniques and tools covered in Mathematics for Machine Learning: PCA are most similar to the requirements found in Data Scientist job advertisements.
Mathematics for Machine Learning: PCA is a part of two structured learning paths.
3 Courses
4 Months
Mathematics for Machine Learning
22 Courses
Free Data Scientist
