This repository contains my Machine Learning learning journey, focused on understanding core ML concepts through hands-on Python code examples. It is designed for beginners who want to learn ML step by step using practical implementations.

This repository covers the fundamentals of Machine Learning, including:

• Machine Learning basics and terminology
• Data preprocessing and feature handling
• Supervised learning algorithms
• Model training, testing, and evaluation
• Hands-on coding using Python libraries

machine-learning-basics/
│
├── data/
│   └── datasets used for practice
│
├── linear_regression/
│   ├── linear_regression.py
│   ├── linear_regression.ipynb
│   └── README.md
│
├── logistic_regression/
│   ├── logistic_regression.py
│   └── logistic_regression.ipynb
│
├── decision_tree/
│   └── decision_tree.ipynb
│ 
├── random_Forest/
│   └── random_Forest.ipynb
│
├── evaluation/
│   └── model_evaluation_metrics.ipynb
│
└── README.md