This repository contains Jupyter Notebooks with explanations and implementation of some Machine Learning, Statistics and Data Analysis methods. Each argument is firstly described and theoretically explained and then implemented without using any external library.
Concepts are more important than efficiency.
I provide my implementation of the methods described in the famous book The Elements of Statistical Learning. I try to follow the chapter progression as much as possible.
Please remember that this code and notebooks are purely for learning purposes and therefore do not constitute a valuable source of information and could contain errors.
- Linear Regression: both frequentist and bayesian approach
- Linear Classification: least squares
- Naive Bayes
- Polynomial Curve fitting: evaluation of models of several degree
- Monte Carlo Method
Some experiments I have done while learning the Tensorflow Machine Learning library.
In the src folder there are some utility functions that I use in the various notebooks, like data generation utilities, pre-processing and so on.