This repository contains a collection of machine learning models, demonstrating various designs and techniques used across different tasks and domains. Each model is implemented to provide a hands-on approach to understanding core concepts in machine learning.
- Regression Models: Used for predictive analysis and estimating relationships between variables.
- Artificial Neural Network (ANN): Fundamental neural networks designed to mimic the human brain, used for a variety of tasks like classification and regression.
- Convolutional Neural Network (CNN): Specialized for image processing and computer vision, used in tasks such as object detection and image recognition.
- Recurrent Neural Network (RNN): Designed for processing sequential data like time series, speech, or text, with specialized models like LSTM.
- Dimensionality Reduction: Used to reduce the number of features in a dataset while preserving important information, improving model performance and visualization.
- Unsupervised Machine Learning: Used for discovering patterns and structures in unlabeled data, often used for exploratory data analysis and anomaly detection.