ML Algorithms From Scratch

A collection of various ML algorithms implemented from scratch as part of my Algorithms, AI and ML Laboratory course.

This serves as my dumping ground for everything I have done throughout this course. In each week's folder you will find:

1. Implementation code for the algorithm (labXX.ipynb)
2. Associated report (report.pdf)
3. Typst code for the reports
4. Matplotlib figures, datasets and other artifacts

Getting Started

With uv (Recommended)

1. Ensure you have uv installed
2. Clone the repo
3. Install dependencies

uv sync

4. Run jupyter

uv run jupyter-lab

With pip

1. Clone the repo
2. Optionally create a virtual environment and activate it
3. Install dependencies

pip install .

4. Run jupyter

jupyter-lab

List of Topics

Topic	Folder Link
Gradient-based Algorithms for Optimization	Week 1
Regression	Week 2
Support Vector Machines	Week 3

Learning from this

I have tried to document the most important learning points in the Jupyter notebook files and the associated reports. Some additional points that I didn't find suitable to write in the report are present in the respective week's README.

Note on accuracy

I am no expert in ML and have taken extensive help from AI while writing the algorithms. I have given my best effort to get the algorithms correct however there might be issues with correctness and accuracy.