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CUMS Machine Learning Apply Dimension Reduction Techniques Case Study
Subject
Programming
School
California University of Management and Sciences
Question Description
Context/Background: The central idea is to use ML algorithms to recognize images, both static and dynamic, without specifically providing any guidance to the computer. This task is achieved by training algorithms to recognize various images while also mapping the feature space of those images to a categorical label or name. This task was first successfully achieved in the 1990s when computers were trained to recognize handwritten digits. The trained algorithm was used by postal services to sort mail and efficiently distribute it to the right destination. We will use the same dataset and problem for this case.
The dataset is called MNIST (Modified National Institute of Standards and Technology). More information on MNIST can be found here: https://www.kaggle.com/c/digit-recognizer Use the only train.csv
To do: The main objective is to write a fully executed R-Markdown program performing dimension reduction techniques PCA, t-SNE, and UMAP on high dimensional image data using MNIST (digits) images that are 28 x 28 pixels resolution.
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