by Zacharias Voulgaris | Released March 2018 | ISBN: None

Dimensionality Reduction is a data science technique for reducing the number of dimensions to a smaller set, while retaining most of the information in the original set. Also known as feature selection, it is an essential part of the data preparation phase of data science, especially when the number of features is very high. Techniques discussed in this video include Components Analysis (PCA), Independent Components Analysis (ICA), Restricted Bolzmann Machine (RBM), and Autoencoders, along with advice on when to select each approach.Here is a link to all of Zacharias Voulgaris' machine learning, data science, and artificial intelligence (AI) videos. Show and hide more

1. Dimensionality Reduction: Using Statistics, Heuristics, and AI Methods 00:27:12
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