leandromineti / Ml Feynman Experience
Licence: mit
A collection of analytics methods implemented with Python on Google Colab
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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, NumPy and SciPy on Google Colaboratory. If you want to play with the code, feel free to copy the notebook and have fun.
Notebooks
- Law of large numbers
- Markov chains
- Single parameter frequentist inference
- Simple linear regression
- Multiple linear regression
Work in progress
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.
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