🧠 Machine Learning & AI Algorithms Repository

Welcome to my Machine Learning & AI Algorithms collection 🚀
This repository is a knowledge hub where I collect, explain, and organize important algorithms used in Artificial Intelligence, Machine Learning, and Search Problems.

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📌 What’s Inside? //

This repository contains explanations of:

🔍 Search Algorithms (AI / Pathfinding)

• Breadth-First Search (BFS)
• Depth-First Search (DFS)
• Uniform Cost Search
• Greedy Best-First Search
• A* Search Algorithm

📊 Machine Learning Algorithms

• Linear Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Trees
• Naive Bayes
• Support Vector Machines (SVM)

🤖 Optimization & Learning

• Gradient Descent
• Perceptron Learning Algorithm
• Backpropagation
• Neural Networks (Basic)

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📂 Repository Structure

ml-algorithms/
│── search/                # AI Search algorithms
│── ml/                    # Machine Learning basics
│── optimization/          # Optimization & learning methods
│── README.md              # This file

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🧭 How to Use This Repository

1. Browse the search folder for classic AI search algorithms.
2. Check the ml folder for traditional machine learning algorithms.
3. Explore optimization to understand learning techniques.
4. Each algorithm is explained with:
   • 📖 Concept
   • 📊 Use case
   • 📝 Pseudocode
   • 🎯 Applications

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🎯 Goals of This Repository

• Serve as a personal learning guide 📚
• Provide a reference for students & developers 🤓
• Act as a base for hackathons and projects 🚀

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📝 Example: A* Algorithm (Documentation Style)

• Problem: Find the shortest path in a graph considering both distance and heuristic.
• Idea: A* uses the formula:

  f(n) = g(n) + h(n)
  

  where:
  • g(n) = cost so far
  • h(n) = heuristic estimate
• Applications: GPS navigation, game AI, robotics path planning.

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🤝 Contribution

• Fork this repository
• Add explanations / algorithms
• Create a pull request

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📜 License

This repository is licensed under the MIT License.