Machine Learning Algorithms

A beginner-friendly collection of basic machine learning algorithm implementations using simple datasets.
This repository includes step-by-step Jupyter notebooks covering fundamental concepts like regression, classification, and clustering — implemented from scratch or using popular libraries like scikit-learn.

This repository is a work-in-progress

---

Repository Structure

• regression/
  Regression models implemented from scratch and with libraries.

• classification/
  Notebooks demonstrating basic classification techniques.

• clustering/
  Examples of clustering algorithms (e.g., K-Means).

• datasets/
  Simple datasets used across the notebooks.

Note: This project is a work in progress. More notebooks and features will be added soon.

---

Steps to run locally

To run the notebooks locally, follow these steps:

1. Clone the repository

git clone https://github.com/yeshapan/machine-learning-algorithms.git
cd machine-learning-algorithms

2. (Optional) Create Virtual Environment

# Create the environment
python -m venv venv

# Activate it
# On macOS/Linux:
source venv/bin/activate

# On Windows:
venv\Scripts\activate

3. Install required packages

4. Launch Juypiter notebook

End Note

This repository is a personal learning resource for building a deeper understanding of core machine learning algorithms. It's a work in progress and will grow over time as new topics are explored. Feel free to explore the notebooks and use them for your own learning! 😊🌼