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Feature Reduction Techniques in ML by anjanag162000 is a document available to read on EtoBox.

The document discusses feature reduction techniques in unsupervised machine learning, focusing on Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). PCA is a linear method that reduces dimensionality while retaining variance, whereas t-SNE is a non-linear technique primarily used for visualizing high-dimensional data. Additionally, it covers Natural Language Processing (NLP) tasks, including supervised, unsupervised, and self-supervised learning, along with steps for

Author

anjanag162000

Language

EN