Understanding Dimensionality Reduction | DataHour by Varun Behl | Data Scientist at Adobe
While building ML/DL models one has to take care of a large volume of data which increases model run time and the complexity to understand the predictions.
Dimensionality reduction is a way to deduce large columns of data to few, the transformed data explains data in a precise manner and makes the process of generating output easy for classic ML models. The process is nothing but the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic…
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Chapters (5)
Introduction to Topic
2:09
What is Dimensionality Reduction
8:25
Principal Component Analysis
21:28
Factor Analysis
31:18
Example of Principal Component Analysis
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