This repository was initially created to submit machine learning assignments for Siraj Raval's online machine learning course. 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 data science and 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.
The eventual goal for this repository is to contain detailed notebooks on statitistical analysis, machine learning, and deep learning with everything coded mainly using numpy and pandas.
I write the notebooks to contain:
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Intuition
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Mathematics and Statistics behind the tool/algorithm
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Code implementation from scratch (using numpy)
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Application to real (publicly available) data
If you spot any mistakes in the code or the theory, feel free to raise an issue.
If you would like to directly contact me with queries related to this repository:
Email: hammy.shaikh@mail.utoronto.ca
Twitter: https://twitter.com/HammadShaikhha