This repository contains implementations of various machine learning models and algorithms in Python. Below is an overview of the models included and how to navigate this repository. For detailed explanations and mathematics used in these models, refer to NumpyAIModels.pdf.
K-Means Clustering is a method for partitioning data into ( K ) distinct clusters. It minimizes the variance within each cluster by iteratively assigning data points to clusters and updating centroids.
Gradient Boosting is an ensemble learning technique that builds models in a stage-wise fashion from weak learners, typically decision trees. It corrects errors from previous iterations to improve overall performance.
The Feed Forward Neural Network is designed for multi-class classification tasks using deep neural networks. It utilizes ReLU activation for hidden layers and softmax for the final layer to predict class probabilities.
Principal Component Analysis is a technique for dimensionality reduction that transforms the data into a new coordinate system, capturing the variance with fewer dimensions.
Decision Trees are non-linear models that split the data based on feature thresholds to make predictions.