Explore the Most Popular Machine Learning Algorithms and How They Work

Machine Learning Algorithm Journey

In this article we introduce the most popular machine learning algorithms, offering two ways to think about and classify them: by learning style or by problem type similarity.

First, group by learning style.

Second, group by similarity of form or function (problem type), like grouping similar animals.

Both are useful, but we focus on similarity‑based grouping and present various algorithm types.

Classification by Learning Style

This classification helps consider the role of input data and model preparation, guiding the choice of the most suitable algorithm for a problem.

Supervised Learning – Training data with known labels (e.g., spam/ham, stock price). The model is trained to predict and is corrected when errors occur, iterating until desired accuracy is reached. Includes classification and regression algorithms.

Unsupervised Learning – Input data without labels; the model discovers structure, often by reducing redundancy or organizing by similarity (e.g., Apriori, K‑Means).

Semi‑Supervised Learning – Uses a mix of labeled and unlabeled data; popular in image classification where large datasets have few labeled examples.

Classification by Problem Type

Algorithms are often grouped by functional similarity.

Regression Algorithms

Regression models the relationship between variables and iteratively improves predictions based on error. Popular regression algorithms include:

Ordinary Least Squares Regression (OLSR)

Linear Regression

Logistic Regression

Stepwise Regression

Multivariate Adaptive Regression Splines

Instance‑Based Algorithms

Instance‑based learning stores examples and predicts new data by measuring similarity to stored instances. Common algorithms are:

K‑Nearest Neighbors (KNN)

Support Vector Machine (SVM)

Regularization Algorithms

Regularization extends other methods (often regression) by penalizing model complexity to favor simpler, more generalizable models. Popular regularization algorithms include:

Ridge Regression

Lasso Regression

Elastic Net Regression

Decision Tree Algorithms

Decision trees build models by splitting on attribute values, useful for classification and regression. Popular decision‑tree algorithms include:

CART (Classification and Regression Trees)

ID3

C4.5 and C5.0

Bayesian Algorithms

Bayesian methods apply Bayes’ theorem to problems such as classification and regression. Popular Bayesian algorithms include:

Naïve Bayes

Gaussian Naïve Bayes

Multinomial Naïve Bayes

Average One‑Dependence Estimators

Bayesian Belief Networks (BBN)

Bayesian Networks (BN)

Clustering Algorithms

Clustering groups data based on inherent structure, often using centroid‑based or hierarchical methods. Popular clustering algorithms include:

K‑Means

K‑Medians

Expectation‑Maximization (EM)

Hierarchical Clustering

Association Rule Learning Algorithms

These algorithms extract rules that explain relationships between variables in large multidimensional datasets. Popular algorithms include:

Apriori algorithm

Eclat algorithm

Artificial Neural Network Algorithms

Neural networks are inspired by biological neurons and are used for pattern matching in regression and classification. Classic methods include:

Perceptron

Multilayer Perceptron (MLP)

Back‑Propagation

Stochastic Gradient Descent

Hopfield Network

Radial Basis Function Network

Deep Learning Algorithms

Deep learning builds larger, more complex neural networks for tasks such as image, text, audio, and video processing. Popular deep learning algorithms include:

Convolutional Neural Networks (CNN)

Recurrent Neural Networks (RNN)

Long Short‑Term Memory (LSTM)

Auto‑Encoders

Deep Boltzmann Machine (DBM)

Deep Belief Network (DBN)

Dimensionality Reduction Algorithms

These methods find and exploit intrinsic data structure to summarize data with fewer dimensions, useful for visualization and simplifying supervised learning. Popular techniques include:

Principal Component Analysis (PCA)

Principal Component Regression (PCR)

Partial Least Squares Regression

Multidimensional Scaling (MDS)

Linear Discriminant Analysis

Ensemble Algorithms

Ensemble methods combine multiple weak models trained independently to produce a stronger overall predictor. Popular ensemble algorithms include:

Gradient Boosting Regression Trees (GBRT)

Random Forest

The purpose of this tour is to give an overview of existing algorithms and some insight into how they relate to each other.

References: https://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/