Welcome to the Machine Learning Algorithms From Scratch repository! This repository is dedicated to those who want to deepen their understanding of machine learning by implementing various algorithms from the ground up. Whether you're a student, a researcher, or an enthusiast, you'll find this collection both educational and practical.
In this repository, you'll find Python implementations of the following machine learning algorithms:
- Density-based spatial clustering of applications with noise (DBSCAN)
- Decision Tree
- EigenFaces
- Gaussian mixture model (GMM)
- k-means clustering
- Logistic Regression
- Naive Bayes
- t-distributed stochastic neighbor embedding (t-SNE)
- k-nearest neighbors (K-NN)
- Random Forest
- Support-Vector Machines (SVM)
Each algorithm is implemented in a clear and concise manner, making it easy to understand the underlying mechanics and theory.
