DataHour: Understanding Dimensionality Reduction
DataHour: Understanding Dimensionality Reduction
22 Nov 202215:11pm - 22 Nov 202216:11pm
DataHour: Understanding Dimensionality Reduction
About the Event
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 dimension.
In this DataHour, Varun will discuss PCA and Factor analysis to understand how dimensional reduction is performed.
Prerequisites: Enthusiasm of learning Data Science and basic understanding of data dimensions, Eigenvalues, EigenVectors and Linear equations
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
Who is this DataHour for?
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
- Best articles get published on Analytics Vidhya’s Blog Space
About the Speaker
Participate in discussion
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