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Chris Farr, Ph.D. is a technology entrepreneur who co-founded an analytics company that was acquired by a firm in Silicon Valley.
This is partly an intro talk, and partly a more mathematical talk. PCA is one of the most popular methods in data science and machine learning. It is used to reduce the dimensionality of data, often as a preprocessing step. This lecture will cover the math of PCA from scratch and then dive into specific applications such as eigenfaces.
His talk is available for download on Github as a Jupyter Notebook. Download the notebook and install python and Jupyter notebook to run the code yourself and follow along interactively. The Anaconda python distribution is highly recommended, it comes bundled with a number of popular and useful packages and Jupyter notebooks are included.
Parts of this lecture were beta tested at the Saratoga Machine Learning & Big Data Analytics Meetup, where it was well received.
Thank you to Enable Labs and John Fitzpatrick for making the Enable Labs space available for this meetup.