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This notebook is an extension from the Python Programming and Numerical Methods - A Guide for Engineers and Scientists, the content is also available at Berkeley Python Numerical Methods.

The text is released under the CC-BY-NC-ND license, and code is released under the MIT license. If you find this content useful, please consider supporting the work on Elsevier or Amazon!

< 25.4 Clustering | Contents | CHAPTER 26. Conclusion and Beyond >

Summary

  1. Machine learning are algorithms that have the capability to learn from data and generalize to the new data.

  2. Machine learning have two main categories supervised learning and unsupervised learning. In supervised learning, there are classification and regression, while in unsupervised learning, there are clustering and dimensionality reduction.

  3. The output of the classification tasks are categorical data.

  4. The output of the regression tasks are quantity data.

  5. The clustering algorithms working on the data that has no labels, they put the data into different groups by finding the patterns of the hidden structure of the data or the similarities.

Problems

None

< 25.4 Clustering | Contents | CHAPTER 26. Conclusion and Beyond >