AI-ML Repository

Welcome to the AI-ML Repository 🎯 — a structured collection of resources, algorithms, codes, and projects covering Artificial Intelligence (AI) and Machine Learning (ML). This repository is designed for learners, researchers, and developers who want a well-organized hub to explore and practice AI/ML concepts.

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📂 Repository Structure

AI-ML/
│── README.md                # Overview of repo
│── requirements.txt         # Python dependencies (scikit-learn, numpy, pandas, etc.)
│
├── AI/                      # Artificial Intelligence concepts
│   ├── Search_Algorithms/   # BFS, DFS, A*, etc.
│   ├── Expert_Systems/      # Rule-based systems
│   └── NLP_Concepts/        # Natural Language Processing basics
│
├── ML/                      # Machine Learning theory + algorithms
│   ├── Algorithms/          # Detailed explanations
│   └── Pipeline/            # ML lifecycle pipeline
│
├── ML_Codes/                # Practical code implementations
│   ├── Regression/          # Linear, Logistic
│   ├── Classification/      # Decision Tree, Random Forest
│   ├── Clustering/          # KMeans, DBSCAN
│   └── README.md
│
├── Projects/                # End-to-end ML projects
│   ├── House_Price_Prediction/
│   ├── Sentiment_Analysis/
│   └── Image_Classification/
│
└── utils/                   # Helper functions for preprocessing, evaluation, etc.

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🚀 Features

• 📘 AI Concepts – Classical AI (search algorithms, expert systems, NLP).
• 🤖 ML Algorithms – Supervised, Unsupervised, Ensemble methods, etc.
• 🧑‍💻 ML Codes – Clean Python implementations of ML algorithms.
• 📊 Projects – End-to-end ML case studies with datasets, notebooks, and models.
• ⚙️ Utilities – Reusable preprocessing, visualization, and evaluation scripts.

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🔹 Machine Learning Pipeline

1. Problem Definition – Identify the objective.
2. Data Collection – Acquire datasets.
3. Data Preprocessing – Clean & transform data.
4. EDA – Visualization and insights.
5. Feature Engineering – Selection and transformation.
6. Model Training & Validation – Build & test models.
7. Hyperparameter Tuning – Optimize performance.
8. Model Evaluation – Metrics like Accuracy, F1, AUC.
9. Deployment – Export & integrate models.
10. Monitoring – Track drift and retrain.

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📚 Algorithms Covered

• Supervised: Linear/Logistic Regression, Decision Trees, Random Forest, SVM, Gradient Boosting, Neural Networks.
• Unsupervised: KMeans, Hierarchical, DBSCAN, PCA, t-SNE.
• Reinforcement: Q-Learning, DQN, PPO.
• Deep Learning: ANN, CNN, RNN, LSTM, Transformers.
• Ensemble Methods: Bagging, Boosting, Stacking.

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🛠️ Installation

Clone this repository and install dependencies:

git clone https://github.com/Skale3628/AI-ML.git
cd AI-ML
pip install -r requirements.txt

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🎯 Usage

Navigate to any section:

• AI/ → Explore AI fundamentals.
• ML/Algorithms/ → Learn theoretical ML algorithms.
• ML_Codes/ → Run algorithm implementations.
• Projects/ → Hands-on ML case studies.

Example (running Linear Regression):

python ML_Codes/Regression/linear_regression.py

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🤝 Contributing

Contributions are welcome! 🙌

• Fork the repo.
• Create a new branch (feature-newalgo).
• Commit changes and open a pull request.

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🙌 Acknowledgments

• Scikit-learn
• TensorFlow
• PyTorch
• Open-source community for continuous learning.

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⭐ If you find this repository helpful, don’t forget to star it on GitHub!