https://github.com/rfsantacruz/ml-nb
IPython Notebooks of machine learning algorithms and applications
https://github.com/rfsantacruz/ml-nb
data-science machine-learning machine-vision python
Last synced: 15 days ago
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IPython Notebooks of machine learning algorithms and applications
- Host: GitHub
- URL: https://github.com/rfsantacruz/ml-nb
- Owner: rfsantacruz
- Created: 2017-04-27T09:39:22.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-04-27T10:44:01.000Z (about 9 years ago)
- Last Synced: 2025-05-22T02:40:58.002Z (about 1 year ago)
- Topics: data-science, machine-learning, machine-vision, python
- Language: Jupyter Notebook
- Homepage:
- Size: 311 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# ml-nb: Machine Learning Notebook
This repository contains a set of IPython Notebooks about machine learning algorithms and its applications. Some of the content is based on the [PRML Book](https://www.microsoft.com/en-us/research/people/cmbishop/?from=http%3A%2F%2Fresearch.microsoft.com%2F%7Ecmbishop%2Fprml%2Findex.htm) by Christopher Bishop.
## Requirements:
The notebooks are developed with:
* Python 3
* Numpy
* SciPy
* Pandas
These libraries can be easily installed using Anaconda and Pip.
## Notebooks
1. [Parametric Density Estimation](./ParametricDensityEstimation.ipynb): Estimating a parametric density distributions.
2. [Nonparametric Density Estimation](./NonparametricDensityEstimation.ipynb): How about non-parametric density estimation.
3. [Linear Regression Models](./LinearRegressionModels.ipynb): Classic linear models for regression in a toy data.
4. [Linear Classification Models](./LinearClassificationModels.ipynb): Linear classification models applied to multi-class classification.
5. [Neural Networks](./NeuralNetworks.ipynb): Exploring non-linearity with Neural networks.
6. [Kernel Trick](./KernelTrick.ipynb): Applying the kernel trink on regression models.