ptzagk / math-of-machine-learning-course-by-siraj

Implements common machine learning algorithms from scratch in python. Intuition and theory behind the algorithms is also discussed.

Math-for-Machine-Learning-Course-by-Siraj

Introduction

This repository was initially created to submit machine learning assignments for Siraj Raval machine learning course on youtube. The purpose of the course was to learn how to implement the most common machine learning algorithms from scratch (without using machine learning libraries such as tensorflow, PyTorch, scikit-learn, etc).

Although that course has ended now, I am continuing to learn machine learning from other sources such as Coursera, online blogs, and attending machine learning lectures at University of Toronto. Sticking to the theme of implementing machine learning algortihms from scratch, I will continue to post detailed notebooks in python here as I learn more.

Style of notebooks

I write the notebooks to contain 1) Intuition, 2) Mathematics behind the algorithm, 3) Code implementation from scratch, and 4) Application to real data.

If you spot any mistakes in the code or the theory, feel free to raise a issues.

References:

Sylabus for mathematics in machine learning course