Machine Learning Notes and Practice

This repository contains my learning work in machine learning.

I organized the content into two main parts:

• Supervised learning
• Unsupervised learning

Most of the work is in Jupyter notebooks, with examples, basic math, and practice on core concepts.

Folder Structure

Machine- Supervised Learning

• 1.maths.ipynb
• 2.ml.ipynb
• 3.Supervised-Learning.ipynb
• 4.Data-Preprocessing.ipynb
• 5.Classiification_Confusion-metrics.ipynb
• 6.MSE-MAE-RMSE.ipynb
• Encode.csv

UnSupervised-Learning

• 1.Unsupervised-Learning.ipynb
• 2.Linear_Algebra_Maths.ipynb
• 4.PCA.ipynb
• 5.Clustering.ipynb

What I Covered

• Basic math used in machine learning
• Data preprocessing steps
• Supervised learning basics
• Classification and confusion-matrix metrics
• Regression error metrics: MSE, MAE, RMSE
• Unsupervised learning basics
• PCA and clustering

How to Run

1. Create and activate a Python virtual environment.
2. Install needed libraries.
3. Open the notebooks and run cells in order.

Example install command:

pip install jupyter numpy pandas matplotlib scikit-learn seaborn

Then start Jupyter:

jupyter notebook

Purpose

This project is for learning and revision. It is a personal practice repository where I keep notes and notebook-based experiments while studying machine learning.