Oreilly - Statistics Series: Principal Component Analysis (PCA) In-depth
by Zacharias Voulgaris PhD | Released January 2019 | ISBN: 9781634624824
Explore PCA with us in detail so you can confidently apply this unsupervised learning method in your data science projects. We cover variance as a proxy for information in a dataset and how to transform the dataset with matrix rotation. How does PCA compare to Singular Value Decomposition (SVD)? Find out in this video, along with exploring the many PCA use cases.Here is a link to all of Zacharias Voulgaris' machine learning, data science, and artificial intelligence (AI) videos. Show and hide more
- Principal Component Analysis (PCA) In-depth 00:23:03
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