Machine Learning 2024

This repository contains a collection of machine learning projects, organized into modules and practical tasks, designed to cover a wide range of machine learning concepts and algorithms. Each folder represents a specific topic in machine learning, with corresponding algorithms and implementations.

📂 Folder Structure

1. Week 2 - Feature Extraction

• Introduction to feature extraction methods.
• Techniques used:
  • Principal Component Analysis (PCA)
  • Singular Value Decomposition (SVD)
  • Feature scaling and normalization.

2. Week 3 - Regression

• Implementation of regression models:
  • Linear Regression
  • Polynomial Regression
  • Ridge and Lasso Regression.
• Evaluation metrics:
  • Mean Squared Error (MSE)
  • R-squared score.

3. Week 4 - Classification

• Exploration of classification algorithms:
  • Logistic Regression
  • Support Vector Machines (SVM)
  • Decision Trees.
• Topics covered:
  • Data splitting and cross-validation.
  • Precision, Recall, and F1-score metrics.

4. Week 6 - Ensemble Learning

• Focus on ensemble methods:
  • Random Forests
  • Gradient Boosted Trees
  • AdaBoost.
• Discussion on improving model performance using bagging and boosting techniques.

5. Week 7 - Clustering

• Unsupervised learning techniques:
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN.
• Evaluation metrics:
  • Silhouette Score
  • Elbow Method.

6. Week 9 - Perceptron & Artificial Neural Networks (ANN)

• Basics of neural networks:
  • Perceptron Algorithm
  • Multi-layer Perceptron (MLP).
• Introduction to deep learning concepts:
  • Activation functions
  • Backpropagation.

🛠️ Tools and Frameworks

• Programming Languages : Python
• Libraries :
• NumPy
• Pandas
• scikit-learn
• TensorFlow/PyTorch
• Matplotlib/Seaborn

📖 Usage

1. Clone the repository:

   git clone https://github.com/dash7ou/machine-learning-2024.git
   cd machine-learning-2024

2. Set up a Python virtual environment:

   python -m venv env
   source env/bin/activate  # On Windows, use `env\Scripts\activate`
   pip install -r requirements.txt

3. Navigate to the respective folder to explore specific topics.

🚀 Highlights

• Step-by-step implementation of various machine learning algorithms.
• Practical examples using real-world datasets.
• Insights into model evaluation and optimization techniques.

🧑‍💻 Contribution

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a branch for your changes.
3. Submit a pull request with detailed information.

🌐 Connect

Explore the projects and share your feedback. Create an issue in the repository for questions or suggestions.