lawlite19/MachineLearning_Python

Features

• Python Machine Learning Libraries - A comprehensive collection of core machine learning algorithms implemented from scratch in Python.
• Centroid-Based Clustering - Implements unsupervised grouping of data by iteratively updating central points.
• Dimensionality Reduction - Simplifies high-dimensional datasets by projecting data onto principal components that preserve maximum variance.
• Gradient-Based Learning - Implements weight updates in neural networks using gradients computed via backpropagation.
• K-Means Clustering - Groups unlabeled data by iteratively assigning points to the nearest centroid.
• Clustering Algorithms - Groups unlabeled data points into distinct clusters based on feature similarity.
• Linear Regression - Implements linear regression to predict continuous values using gradient descent and feature normalization.
• Logistic Regression Models - Performs binary classification by minimizing a convex cost function through optimization methods.
• Neural Network Frameworks - Implements a neural network framework featuring multi-layer perceptrons with backpropagation and regularization.
• Neural Network Implementations - Implements multi-layer neural networks using forward propagation and gradient descent for training.
• Neural Networks - Implements a neural network framework for creating multi-layer perceptrons with backpropagation.
• Gradient Descent Algorithms - Uses iterative gradient descent to minimize cost functions and optimize model weights.
• Supervised Classification - Provides a full workflow for building and training models to categorize data into distinct classes.
• Supervised Learning - Provides a set of supervised learning models including linear regression, logistic regression, and support vector machines.
• Support Vector Machines - Constructs classifiers that find the maximum margin hyperplane to separate two distinct classes.
• Unsupervised Learning - Implements unsupervised learning algorithms including k-means clustering and principal component analysis.
• Anomaly Detection - Provides statistical anomaly detection for identifying outliers and rare events in datasets.
• Toolkits - Ships a specialized anomaly detection toolkit using Gaussian distributions and F1-score optimization.
• Principal Component Analysis - Implements a full principal component analysis algorithm to reduce dimensionality while maximizing variance.
• Anomaly Detection Metrics - Measures the accuracy of anomaly detection using precision, recall, and F1-score.
• Gradient Checking - Validates backpropagation gradients by comparing analytic results against numerical approximations.
• Decision Threshold Calibration - Optimizes probability thresholds on cross-validation sets to maximize the F1-score for anomaly detection.
• Feature Scale Normalization - Scales numerical features to a consistent range using mean and standard deviation for faster model convergence.
• Probability Density Functions - Calculates probability density values using mean and variance to model normal data distributions.
• Kernel-Based Feature Mapping - Transforms data into higher-dimensional spaces using kernel functions to resolve non-linear patterns.
• Polynomial Feature Mapping - Transforms low-dimensional input features into polynomial combinations to capture non-linear relationships.
• Neural Network Construction - Implements the process of building neural networks through random weight initialization and automated gradient checking.
• Neural Network Regularization - Prevents model overfitting by adding a penalty term based on weight magnitudes to the cost function.
• Digit Recognition - Processes image pixel data to identify and recognize handwritten numeric characters.
• Kernel SVMs - Implements support vector machines that use kernel functions for non-linear separation.
• Weight Decay Regularization - Prevents overfitting by adding a penalty term proportional to the square of the weights to the cost function.
• Weight Initialization - Assigns small random values to weights to break symmetry and allow neurons to learn distinct features.
• Weight Regularization - Prevents model overfitting by adding a penalty term based on the square of the weights to the cost function.
• Gaussian Anomaly Detectors - Implements statistical outlier detection using Gaussian probability density functions.
• One-Vs-All Multi-class Classification - Solves multi-class classification problems by training a binary classifier for each distinct category.
• Non-Linear Classifiers - Uses linear, polynomial, and Gaussian kernels to separate non-linearly distributed datasets.

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