Machine Learning Open Cookbook

This repository contains Machine Leanring implementations for anyone to study. This is suppose to be used as teaching or self-study material.

Contents

1. Linear Regression.

   a. Implementation of LinearRegression class by means of normal equations and the gradient descent algorithm.

   b. Use of the LinearRegression class to make non-linear regression problems.

   c. Implementation of RidgeRegression class to tackle the problem of overfitting

2. Logistic Regression: a gradient descent implementation.

3. ...

Solutions vs homeworks

Each method, model or algorithm has its own folder, in which you can find, among others, the folowing files:

• lib.py, which contains the class and function definitions.
• notebook_[XX].ipynb, where we can test our implementations and hopefully help you gain some insights along the way. The XX is used to denote the order in which it should be read.

We use the homework package to make the necessesary files for this repository (homeworks and solutions). This help us easily create the programming assignments by implementing the working version of the library.

How to contribue

1. By finding errors in the implementation or discussion and reporting them via an issue (hopefully a PR).
2. Adding new algorithms to the book, for which you will need to make the homework

Contributors

This Machine Learning Open Cookbook is possible with the help of the following enthusiasts:

• srcolinas