Artificial Intelligence Specialization

This repository contains two machine learning projects: a Linear Regression analysis on wave energy farms and a Convolutional Neural Network (CNN) for image classification.

📚 Documentation

Detailed analysis for both projects can be found in the docs directory:

• Linear Regression: docs/ml_portfolio.pdf
• Neural Network: docs/cnn.pdf

🛠️ Built With

• Pandas: data manipulation and analysis
• Numpy: numerical computing tols
• Plotly: interactive data visualization
• Matplotlib & Seaborn: static data visualization
• Numba: just-in-time compilation for performance optimization
• Tensorflow: machine learning framework

📊 Linear Regression: Wave Energy Farm Analysis

Data Source

• Dataset: "Large-scale Wave Energy Farm"
• Origin: UCI Machine Learning Repository
• Contributors: Researchers from University of Adelaide and Monash University
• Date Added: September 16, 2023
• Instances: 63,600 unique wave energy converter configurations

🚀 Quick Start

Prerequisites

• Python 3.11
• pip (Python package manager)

Setup

1. Clone the repository:

git clone https://github.com/salgue441/artificial-intelligence
cd artificial-intelligence

2. Install the required packages:

pip install -r requirements.txt

Run the Analysis

To execute the analysis, run the following command:

cd regression/src
python main.py

🖼️ Neural Network: Intel Image Classification

Data Source

• Dataset: Intel Image Classification
• Origin: Analytics Vidhya Challenge
• Instances: 25,000 images of natural scenes

🚀 Quick Start

Prerequisites

• Python 3.11
• Cuda 11.2
• cuDNN 8.2

Setup

1. Ensure you're in the project root directory:

cd artificial-intelligence

2. Install the required packages:

pip install -r requirements.txt

3. Start the Jupyter Notebook server:

jupyter notebook neural-network/nn.ipynb

📄 License

Distributed under the MIT License. See LICENSE for more information.