Machine Learning Algorithms

This repository contains basic implementations of Machine Learning Algorithms.

Table of Contents

1. Decision Trees
2. Discriminative Lineae Models (DLMs)
3. Generalized Linear Models (GLMs)
4. Support Vector Machines (SVMs)
5. Neural Networks

Decision Trees

Decision Trees are a type of Supervised Machine Learning where the data is continuously split according to a certain parameter.

Discriminative Lineae Models (DLMs)

A Discriminative Linear Model (DLM) is a type of model used in machine learning for classification or regression tasks. It is a class of logistical models that distinguish decision boundaries through observed data.

Generalized Linear Models (GLMs)

Generalized Linear Models (GLMs) are a flexible generalization of ordinary linear regression that allows for response variables with error distribution models other than a normal distribution.

Support Vector Machines (SVMs)

Support Vector Machines (SVMs) are supervised learning methods used for classification, regression, and outliers detection.

Neural Networks

Neural Networks are a class of machine learning models inspired by the structure and function of the human brain. They consist of interconnected nodes organized in layers, with each node performing a simple computation. Neural networks have gained popularity due to their ability to learn complex patterns from data.

Multilayer Perceptron (MLP)

A Multilayer Perceptron (MLP) is a feedforward artificial neural network model that consists of multiple layers of nodes, each connected to the next in a feedforward manner. MLPs are capable of learning nonlinear relationships in data and are widely used for various tasks such as classification and regression.

Unsupervised Learning

Unsupervised Learning is a type of machine learning that deals with unlabeled data, meaning the data inputs are not associated with any outcomes or targets. The goal of unsupervised learning is to find underlying patterns, structures, or relationships within the data.

Anomaly Detection

Anomaly detection is a technique that is used to identify unusual patterns that do not conform to expected behavior, called outliers.

Clustering

Clustering is a technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group.

Recommendation Systems

Recommendation systems are a type of information filtering system that are commonly used in different online applications where they provide suggestions to the user on what they might like to choose.

Reinforcement Learning

Reinforcement Learning is an aspect of machine learning where an agent learns to behave in an environment, by performing certain actions and observing the results/outcomes of those actions

Contributing

Just to let you know, pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.