https://github.com/simranjitk/simplifying-data-for-machine-learning
Learn how to best simplify data to use as input for machine learning models
https://github.com/simranjitk/simplifying-data-for-machine-learning
Last synced: about 1 year ago
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Learn how to best simplify data to use as input for machine learning models
- Host: GitHub
- URL: https://github.com/simranjitk/simplifying-data-for-machine-learning
- Owner: Simranjitk
- Created: 2018-11-04T18:00:26.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-11-04T18:08:55.000Z (over 7 years ago)
- Last Synced: 2025-02-26T00:28:29.870Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 104 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Simplifying-Data-for-Machine-Learning
Learn how to best simplify data to use as input for machine learning models
#### Why does dimensionality reduction matter?
1. Space efficiency
2. Computing efficiency
3. Visualization
We will compare 3 dimensionality reduction methods (PCA, T-SNE, and LDA)
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Code Reference: [Gergo Bohner](https://github.com/gbohner) and [Siraj Raval](https://www.youtube.com/watch?v=K796Ae4gLlY&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3&index=10)