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Introduction to Unsupervised Machine Learning, number of approaches to unsupervised learning such as K-means clustering, hierarchical agglomerative Clustering and its applications.

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Unsupervised_Machine_Learning

Lab1: Introduction to Unsupervised Learning

Lab Steps

  1. Make sure that you have completed the setup requirements as described in requirement.txt.
  2. Now, run jupyter notebook and open the “IntroToUnsupervisedLearning.ipynb” notebook under this project.
  3. Examine the notebook and answer the questions along the way.

Question1: Looking at the scatter plots, which model seems to perform the best in clustering the sample data?

Lab2: Application of Clustering

Lab Steps

  1. Make sure that you have completed the setup requirements as described in requirement.txt.
  2. Now, run jupyter notebook and open the “ApplicationOfClustering.ipynb” notebook under this project.
  3. Examine the notebook and answer the questions along the way.

Question1: Which K results in the highest SC?
Question2: Which cluster size results in the highest SC?

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Introduction to Unsupervised Machine Learning, number of approaches to unsupervised learning such as K-means clustering, hierarchical agglomerative Clustering and its applications.

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