Authoritative References & Courses
9 entriesDocumentation, courses and free books.
01scikit-learn: Choosing the right estimatorFlowchart for choosing an algorithm for a problem.
02scikit-learn: Supervised learningUser guide to supervised algorithms.
03scikit-learn: Unsupervised learningUser guide to clustering, dimensionality reduction and more.
04scikit-learn: Ensemble methodsBagging, boosting, random forests and stacking.
05Machine Learning Crash Course (Google)Free course with videos and exercises.
06Stanford CS229: Machine LearningStanford's machine learning course materials.
07An Introduction to Statistical LearningFree textbook on statistical learning methods.
08Dive into Deep LearningFree interactive book on deep learning with code.
09Deep Learning (Goodfellow, Bengio, Courville)Free online textbook on deep learning.
Guides & Tours
14 entriesArticles that survey the main algorithm families.
10A Tour of Machine Learning Algorithms by Jason BrownleeA comprehensive tour through the main classes of machine learning algorithms, grouped by learning style and by function.
11Machine Learning Algorithms for BeginnersAn accessible rundown of the machine learning algorithms every data science beginner should know.
12Machine Learning Algorithms by UpasanaA practical walkthrough of the most widely used machine learning algorithms and how they fit into the ML workflow.
13The Top Ten ML Algorithms Every Beginner Has to Know by James LeA beginner-friendly tour of the ten machine learning algorithms most commonly used by data scientists.
14Top Machine Learning Algorithms Used by Data ScientistsAn in-depth look at the top algorithms data scientists rely on, with explanations of how each one works.
15Explaining Machine Learning Algorithms in Detail by GeeksforGeeksA detailed reference covering the theory and implementation of core machine learning algorithms.
16A Perspective on Machine Learning Algorithms by Martin HellerAn InfoWorld deep dive explaining the algorithms that power modern AI and machine learning systems.
17A Blog on the Four Types of ML Algorithms by Oleksandr BushkovskyiAn overview of the four major types of machine learning algorithms: supervised, unsupervised, semi-supervised, and reinforcement.
18Comparing Different Supervised Machine Learning Algorithms for Disease PredictionA peer-reviewed BMC study comparing the performance of supervised machine learning algorithms across disease prediction research.
19An Introductory Primer on Machine Learning Algorithms by Nick McCreaA visual, example-driven primer from Toptal covering the theory behind supervised and unsupervised machine learning.
20The Classification of Machine Learning Algorithms for BeginnersA structured overview of how machine learning algorithms are classified by learning style and task.
21A Blog on Top Machine Learning Algorithms & How To Get StartedA BMC guide walking through popular machine learning algorithms and the tools needed to get started.
229 Key Machine Learning Algorithms Explained in Plain English by Nick McCullumA no-code introduction to nine of the most important machine learning algorithms, explained in plain English.
23A Gentle Introduction to Machine Learning AlgorithmsA beginner's guide covering supervised, unsupervised, semi-supervised, and reinforcement learning algorithms with real-world examples.
Related Pages
6 entriesNeighbouring pages on this site.
24Machine LearningMachine learning hub.
25Machine Learning ModelsRegression, trees, forests and other models.
26Machine Learning FrameworksML frameworks.
27Machine Learning ApplicationsUse cases across industries.
28AutoML ToolsAutomated machine learning.
29Reinforcement Learning AlgorithmsValue-based and policy-based RL.
Frequently Asked Questions
What are the main types of machine learning algorithms?
Supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), ensemble methods that combine models, and reinforcement learning.
How do I choose an algorithm?
Start from the problem type and data size, use a guide such as scikit-learn's estimator map, and compare a few candidates with cross-validation.
Why were some links removed?
Three of the earlier blog links (Dezyre, EliteDataScience and New Tech Dojo) no longer worked, so authoritative references and courses were added.
How current is this page?
Links were checked in October 2026. Sites that block automated checks are kept and may need a manual click. Last-commit dates come from GitHub in October 2026 and show default-branch activity only; check each project's release notes before adopting it.