Supervised Learning Exercises

This repository contains Jupyter Notebook exercises used in Udacity's Supervised Learning course. Each folder covers a key algorithm or concept in supervised learning, with hands-on practice to build practical understanding.

📁 Directory Structure

• Decision_Trees/ – Exercises on classification and regression using decision trees
• Ensemble_Methods/ – Practice with ensemble techniques like bagging, boosting, and random forests
• Finding_Donors_Project/ – Capstone-style project applying supervised learning models to predict donor behavior
• Linear_Regression/ – Exercises covering regression basics and regularization concepts
• Model_Evaluation_Metrics/ – Understanding metrics such as accuracy, precision, recall, F1-score, and ROC curves
• Naive_Bayes/ – Probabilistic classification with the Naive Bayes algorithm
• Perceptron_Algorithm/ – Implementing and evaluating the Perceptron learning algorithm
• Support_Vector_Machines/ – Hands-on exercises with SVMs and different kernel functions
• Training_and_Tuning/ – Focused practice on hyperparameter tuning, validation, and learning curves

🛠 Getting Started

To run the exercises locally:

1. Make sure you have Python 3.x and Jupyter Notebook or JupyterLab installed.

2. Install common data science libraries (if not already installed):

   pip install numpy pandas matplotlib scikit-learn

3. Clone this repository:

   git clone https://github.com/yourusername/supervised-learning-exercises.git
   cd supervised-learning-exercises

4. Launch Jupyter Notebook:

   jupyter notebook

5. Navigate to any directory and start exploring the notebooks.

📜 License

This repository is intended for educational purposes as part of the Udacity Nanodegree program.

See LICENSE.txt for details.