Machine Learning Tools

A comprehensive collection of machine learning tools and algorithms implemented across various techniques and models. This repository serves as a toolkit for exploring, learning, and implementing foundational ML concepts.

## Repository Contents

• Gaussian MatLab: Gaussian-related models and implementations in MATLAB.
• Gradient Descent: Optimization algorithms for gradient-based learning.
• Kernels: Tools for working with kernel methods, including SVM and other kernel-based models.
• Logistic Regression LASSO: Logistic regression models with LASSO regularization.
• Principal Component Analysis: Implementation of PCA for dimensionality reduction.
• Regression: General regression models and techniques.
• SDL MNIST Dataset: Tools and datasets related to MNIST.
• Statistical Inference: Statistical methods and inference techniques.
• Transformers: Transformer-based models for NLP and other tasks.
• k-means Clustering: K-means clustering with applications to pulsar data.

Getting Started

Clone the repository:

git clone https://github.com/davidomanovic/machine-learning-tools.git
cd ml-toolbox