/ML

Machine learning repository exploring various algorithms.

Primary LanguagePythonMIT LicenseMIT

Machine Learning

This repository contains resources and projects related to machine learning.

What is Machine Learning?

Machine Learning (ML) is a subset of artificial intelligence that focuses on the development of algorithms and statistical models that enable computer systems to improve their performance on a specific task through experience. In other words, ML allows computers to learn and make decisions or predictions without being explicitly programmed.

The core idea behind machine learning is to create models that can automatically learn patterns from data and use those patterns to make predictions or decisions on new, unseen data.

Types of Machine Learning

There are three main types of machine learning:

  1. Supervised Learning

    • In supervised learning, the algorithm learns from labeled data.
    • The model is trained on a dataset where the correct output is provided for each input.
    • Examples include:
      • Classification (e.g., spam detection, image recognition)
      • Regression (e.g., price prediction, weather forecasting)

  2. Unsupervised Learning

    • Unsupervised learning deals with unlabeled data.
    • The algorithm tries to find patterns or structures in the data without predefined labels.
    • Examples include:
      • Clustering (e.g., customer segmentation, anomaly detection)
      • Dimensionality reduction (e.g., feature extraction, visualization)

Contents of this Repository

This repository contains implementations of various machine learning algorithms. The algorithms are organized into categories based on their types and applications. Here's an overview of the contents:

  1. Supervised Learning Algorithms

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

  2. Unsupervised Learning Algorithms

    • K-Means Clustering
    • Hierarchical Clustering
    • Principal Component Analysis (PCA)
    • Independent Component Analysis (ICA)

  3. Ensemble Methods

    • Bagging
    • Boosting (AdaBoost, Gradient Boosting)
    • Stacking

  4. Dimensionality Reduction Techniques

    • t-SNE (t-Distributed Stochastic Neighbor Embedding)
    • UMAP (Uniform Manifold Approximation and Projection)

Each algorithm is implemented in both Python and R, providing a comprehensive resource for machine learning practitioners and researchers. The implementations include:

  • Clean, well-commented code
  • Example usage with sample datasets
  • Performance metrics and evaluation techniques
  • Visualization of results where applicable

Note: This repository is continuously updated with new algorithms and improvements to existing implementations. Check back regularly for the latest additions and enhancements.

Getting Started

Prerequisites

  • Python 3.7 or higher
  • R 4.0.0 or higher
  • pip (Python package installer)
  • R package manager

Installation

For Python:

  1. Clone the repository:
git clone https://github.com/A-A7med-i/ML.git
  1. (Optional) Create and activate a virtual environment:
python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows
  1. Install required packages:
pip install -r requirements.txt

For R:

  1. Open R or RStudio

  2. Install the required libraries by running the following commands:

install.packages(c("caret", "nnet", "rpart", "randomForest", "ipred", "MASS", "gbm", "xgboost", "mlbench", "glmnet", "e1071", "datasets"))

Usage

Running Python Scripts

To run a specific Python project:

python projects/project_name/main.py

Contributing

Contributions to this project are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License.