"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.
- Law of large numbers
- Markov chains
- Single parameter frequentist inference
- Simple linear regression
- Multiple linear regression
- 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
If you spot a mistake or omission, please feel free to create a new issue.
- 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.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent