Basics Of Principal Component Analysis Part-1 Explained in Hindi ll Machine Learning Course

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TLDRThis video explains the concept of Principal Component Analysis (PCA) in a simple manner. The presenter aims to make a complex topic more accessible by explaining why and how PCA is used. The main motivation for PCA is to address the problem of overfitting in data models. In overfitting, the model gets confused with too many features, leading to complex and unreliable outputs. PCA reduces the dimensionality of the data, which simplifies the model and helps overcome overfitting. The presenter uses examples to show how PCA transforms high-dimensional data into lower dimensions, leading to a more focused and effective model. Additionally, PCA requires finding principal components which are independent and orthogonal. The presenter encourages viewers to understand prerequisites like mean, variance, eigenvalues, and eigenvectors to further grasp PCA in subsequent videos.

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