Machine Learning Algorithms from Scratch

This repository contains Python implementations of fundamental machine learning algorithms: Linear Regression, K-means, and PCA using Singular Value Decomposition (SVD).

Table of Contents

• Linear Regression
• K-Means
• PCA using SVD
• Getting Started
• Dependencies
• Usage
• Contributing
• License

Linear Regression

The linear regression implementation is in the Liner Regression/Linear_Regression.ipynb file. It includes a simple linear regression model along with functions for training and making predictions.

K-Means

The K-Means implementation is in the K-Means/K_means.ipynb file. The code provides a basic K-Means clustering algorithm that can be used for unsupervised learning tasks.

PCA using SVD

The PCA implementation using Singular Value Decomposition (SVD) is in the PCA/PCA_using_SVD.ipynb file. This script demonstrates how to perform principal component analysis using SVD.

Getting Started

To get started, clone this repository to your local machine or run in google colab:

git clone https://github.com/sm1899/Ml-algos-from-Scratch.git

## Dependencies
Ensure you have the following dependencies installed:

NumPy
Pandas
Scikit-learn

pip install numpy pandas scikit-learn
# Add other dependencies as necessary