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.Lee mas.
Este recurso es ofrecido por un socio afiliado. Si paga por la capacitación, podemos ganar una comisión para respaldar este sitio.
Las técnicas y herramientas cubiertas en Mathematics for Machine Learning: PCA son muy similares a los requisitos que se encuentran en los anuncios de trabajo de Científico de datos.
Mathematics for Machine Learning: PCA is a part of dos structured learning paths.
3 Courses
4 Months
Mathematics for Machine Learning
22 Courses
Free Data Scientist
