Machine Learning 📚🤖

This repository contains a structured collection of machine learning algorithms, ranging from basic to advanced. It serves as a practice ground for implementing and understanding various ML concepts.

📌 Topics Covered

Supervised, Unsupervised, and Reinforcement Learning

Supervised Learning

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. Support Vector Machine (SVM)
5. Naive Bayes
6. K-Nearest Neighbors (KNN)
7. Ensemble Learning
   • Bagging: Random Forest
   • Boosting: AdaBoost, Gradient Boosting, XGBoost

Unsupervised Learning

1. K-Means (Clustering)
2. Dimensionality Reduction

Reinforcement Learning

1. Q-Learning
2. Markov Decision Process (MDP)

Additional Topics

• Optimization Techniques: Gradient Descent, Hyperparameter Tuning
• Feature Engineering & Preprocessing
• Advanced Topics: Neural Networks, Deep Learning, Reinforcement Learning

🛠 Tech Stack

• Programming Language: Python
• Libraries: NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch, Matplotlib

🚀 Getting Started

1. Clone this repository:

   git clone https://github.com/your-username/machine-learning.git

2. Navigate to the project directory:

   cd machine-learning

3. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On macOS/Linux
   venv\Scripts\activate  # On Windows

4. Install dependencies:

   pip install -r requirements.txt

5. Run the scripts for different algorithms as needed:

   python scripts/linear_regression.py