🧠 ML Basics

A beginner-friendly repository covering the fundamentals of Machine Learning using Python and Jupyter Notebooks.

This project is designed to help you understand core ML concepts through hands-on examples, including data exploration, regression, classification, clustering, and neural networks.

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📌 Contents

This repository includes:

• 📊 Data Exploration

  • Understanding datasets
  • Data cleaning and visualization

• 📈 Regression

  • Linear regression models
  • Predicting continuous values

• 🧮 Classification

  • Binary and multi-class classification
  • Model evaluation techniques

• 🔍 Clustering

  • Unsupervised learning
  • Grouping similar data points

• 🤖 Neural Networks

  • Deep learning basics
  • Implementations using frameworks like TensorFlow / PyTorch

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🗂️ Project Structure

• Neual Network
• Classifiers
• Gradient Descent
• Linear Regression
• Logistic Regression
• Some Pracise Questions
• Perceptron Algorithm
• Sigmoid