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.阅读更多.
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Mathematics for Machine Learning: PCA 中涵盖的技术和工具与 数据科学家 招聘广告中的要求最为相似。
Mathematics for Machine Learning: PCA is a part of 二 structured learning paths.
3 Courses
4 Months
Mathematics for Machine Learning
22 Courses
Free Data Scientist
