zotroneneis/machine_learning_basics

Features

• From-Scratch ML Model Implementations - Provides foundational implementations of machine learning models built from first principles without relying on high-level frameworks.
• Machine Learning Education - Serves as an educational resource for learning the mathematics of ML by building algorithms from scratch.
• Centroid-Based Clustering - Implements K-Means clustering by iteratively refining center coordinates based on the mean of assigned points.
• Classification Trees - Implements decision trees specifically for categorizing instances into discrete classes through binary splits.
• Gradient-Based Parameter Updates - Implements weight adjustment logic using gradient descent to minimize prediction error.
• Hyperplane Margin Maximization - Implements the mathematical logic for maximizing margins in Support Vector Machines.
• K-Means Clustering - Implements centroid-based partitioning of unlabeled data into a specified number of clusters using iterative refinement.
• K-Nearest Neighbor Classifiers - Implements supervised learning models that assign classes based on the majority vote of the closest training samples.
• Linear Regression - Implements fundamental statistical methods for modeling relationships between variables using linear equations.
• Bayesian Regressions - Implements probabilistic linear models that treat parameters as distributions to quantify prediction uncertainty.
• Logistic Regression Models - Implements algorithms for predicting binary outcomes using the sigmoid function and weight optimization.
• Machine Learning Implementations - Provides a comprehensive collection of core machine learning algorithms implemented manually in Python.
• Forward Propagation Engines - Implements forward propagation logic using matrix multiplication to calculate layer outputs.
• Neural Network Implementations - Implements an artificial neural network from scratch, demonstrating forward propagation and weight updates.
• Supervised Classification - Implements supervised classification and regression models including Decision Trees and SVMs.
• Support Vector Machines - Implements support vector machines to establish optimal decision boundaries that maximize the margin between classes.
• Unsupervised Learning - Implements unsupervised learning algorithms like K-Means to discover patterns in unlabeled data.
• Educational Python Implementations - Provides algorithmic implementations in pure Python to ensure the internal logic and mathematics are transparent and readable.
• Softmax Regression - Provides an implementation of softmax regression for multi-class classification probability prediction.
• NumPy-Based Linear Algebra Implementations - Uses NumPy for vectorization and matrix manipulations to perform efficient mathematical operations on datasets.
• Perceptrons - Implements a basic linear classifier that learns a weight vector to separate two classes of data.
• Posterior Distribution Estimators - Implements Bayesian linear regression by calculating posterior probabilities for linear coefficients using prior beliefs.
• Dimensionality Reduction - Provides mathematical techniques for reducing high-dimensional data into lower-dimensional spaces based on variance.
• Principal Component Analysis - Implements Principal Component Analysis to reduce dimensionality by transforming variables into uncorrelated components.

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