https://github.com/leandromineti/ml-feynman-experience

A collection of analytics methods implemented with Python on Google Colab
https://github.com/leandromineti/ml-feynman-experience

machine-learning notebook numpy python scipy statistics

Last synced: 3 days ago
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A collection of analytics methods implemented with Python on Google Colab

• Host: GitHub
• URL: https://github.com/leandromineti/ml-feynman-experience
• Owner: leandromineti
• License: mit
• Created: 2018-11-27T12:44:00.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-03-16T13:12:37.000Z (over 4 years ago)
• Last Synced: 2024-10-31T08:42:01.284Z (10 days ago)
• Topics: machine-learning, notebook, numpy, python, scipy, statistics
• Homepage:
• Size: 1.85 MB
• Stars: 219
• Watchers: 14
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine Learning Feynman Experience



> "What I cannot create, I do not understand" - Feynman.

This is a collection of concepts I tried to implement using only [Python](https://www.python.org/), [NumPy](http://www.numpy.org/) and [SciPy](https://www.scipy.org/scipylib/index.html) on [Google Colaboratory](https://colab.research.google.com/notebooks/welcome.ipynb). If you want to play with the code, feel free to copy the notebook and have fun.

## Notebooks

- [Law of large numbers](https://colab.research.google.com/drive/1OrXdXaz7gloahVuv6aIgXwCgs6C7S_oE)

- [Markov chains](https://colab.research.google.com/drive/104V2fY3wQc5m0af_xm7DsRNgpiVPh8x-)

- [Single parameter frequentist inference](https://colab.research.google.com/drive/1rFwHTN7OyrjhOKUY3P3d9C0Rz1d6sad7)

- [Simple linear regression](https://colab.research.google.com/drive/1NUtc-TWBTe2XVD2xdkjwPD4sc43Ozf0g)

- [Multiple linear regression](https://colab.research.google.com/drive/1DMmQ_aVQhRZ7bwIgFFSElCxgTpWCt74f)

### Work in progress

- [Generalized linear models](https://colab.research.google.com/drive/1tJQfD2IGNBhRbnksOPlC0ugNjGEBKC6i)

- [Expectation maximization](https://colab.research.google.com/drive/1nqlu2-0uei2Wmck781EHuW-N2JpfSIG7)

### To do

- \[ \] Principal component analysis

- \[ \] Linear discriminant analysis

- \[ \] Central limit theorem

- \[ \] Single parameter bayesian inference

- \[ \] Decision tree

- \[ \] Random Forest

- \[ \] Support vector machine

- \[ \] Perceptron

- \[ \] Gradient boosting machine

- \[ \] Autoregressive models

### Contributions

If you spot a mistake or omission, please feel free to create a new issue.

### References

- Casella, G., & Berger, R. L. (2002). Statistical inference (Vol. 2). Pacific Grove, CA: Duxbury.

- Costa, M. A. (2019). Tópicos em ciência dos dados: Introdução aos modelos paramétricos e seus aplicações utilizando o R. Bonecker.

- DeGroot, M. H., & Schervish, M. J. (2012). Probability and statistics. Pearson Education.

- Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed). New York, NY: Springer.

- **Cover image**: Dr. Richard Feynman during the Special Lecture: *the Motion of Planets Around the Sun*. Public Domain. Created: 13 March 1964.