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unsupervised-machine-learning

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Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Used multiple algorithms and both empirically and theoretically compared and contrasted their results. Made predictions about the natural categories of multiple types in a dataset, checked predictions against the result of unsupervised analysis.

  • Updated Jan 8, 2018
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Built text and image clustering models using unsupervised machine learning algorithms such as nearest neighbors, k means, LDA , and used techniques such as expectation maximization, locality sensitive hashing, and gibbs sampling in Python

  • Updated Jan 20, 2018
  • Jupyter Notebook

This repository contains all the Machine Learning and Deep Learning projects that I worked on, spans across the two sub domains of Artificial Intelligence i.e., Computer Vision and Text Processing as a part of Machine Learning Nano Degree program at Udacity.

  • Updated Feb 22, 2018
  • Jupyter Notebook

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