Machine Learning

Learn machine learning fundamentals, how models learn from data, and the main ML types (supervised, unsupervised, reinforcement). Designed for beginners.

 

Diagram showing multiple machine learning models combining their predictions in an ensemble.

Ensemble Learning Explained: Beginner-Friendly Guide

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What Is Ensemble Learning? Ensemble learning is a model-combination strategy, not a separate machine learning paradigm. Supervised, unsupervised, and reinforcement learning describe different learning settings. Ensemble methods describe how predictions from two or more models are combined within a suitable setting. This guide focuses mainly on ensembles for supervised classification and regression. Reviewed: October 3,

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Illustrative K-Nearest Neighbors classification example with five selected neighbors: three Class B triangles and two Class A circles

K-Nearest Neighbors Explained: A Beginner’s Guide

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K-Nearest Neighbors (KNN) is a supervised machine-learning method that predicts an outcome from the labeled examples closest to a new data point. It can classify categories or estimate numerical values without fitting a conventional parametric model. The idea is intuitive: nearby examples get a vote. But useful KNN results depend on how “nearby” is defined,

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Educational infographic explaining the Naive Bayes machine learning algorithm and probability-based classification.

Naive Bayes Explained: Beginner-Friendly Guide

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Naive Bayes is a supervised classification method that combines a class’s prior probability with evidence from the features. Its simplifying assumption is conditional independence given the class—not that features are unrelated in the real world. This beginner guide explains Bayes’ theorem, the main variants, smoothing, text representations, evaluation, and when this fast probabilistic classifier is

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Selected stages of hierarchical clustering: agglomerative clustering merges individual observations into larger nested groups; divisive clustering splits one group into smaller nested groups.

Hierarchical Clustering Explained: A Beginner’s Guide

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Hierarchical clustering is an unsupervised method that organizes observations into a nested tree of groups. Instead of returning only one flat partition, it shows how individual observations and larger clusters join—or split—at different levels of dissimilarity. The tree is displayed as a dendrogram. It can help you explore possible groupings, but the result depends on

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Educational infographic showing how K-Means clustering groups similar data points into clusters around centroids.

K-Means Clustering Explained for Beginners

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K-Means is an unsupervised machine-learning algorithm that partitions numerical data into K clusters. It repeatedly assigns each point to a centroid and updates those centroids to reduce variation within the clusters. Before this lesson: Unsupervised Learning Explained introduces the broader idea of finding structure in unlabeled data. In standard K-Means, “closest” has a precise meaning:

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Linear SVM on an illustrative separable dataset. A solid decision boundary lies halfway between two dashed margin boundaries. Three ringed support vectors sit exactly on the dashed boundaries. The double-headed arrow spans the full margin width, not the half-margin.

Support Vector Machines (SVM) Explained for Beginners

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Support Vector Machines, usually shortened to SVMs, are supervised machine-learning methods used mainly for classification, although related methods can also perform regression and novelty detection. An SVM tries to find a decision boundary that separates classes while leaving a useful margin between that boundary and the closest training examples. Those influential training examples are called

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Diagram showing subscription-renewal training data feeding multiple diversified decision trees whose outputs are aggregated into one Random Forest prediction.

Random Forest Explained: Beginner-Friendly Guide

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Random Forest is a supervised machine learning algorithm that combines many randomized decision trees. Its central idea is simple: build trees that make somewhat different errors, then combine their predictions so that tree-specific mistakes can cancel out. If you have not met a decision tree yet, start with Decision Trees Explained. Random Forest adds two

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Illustrative decision tree for predicting whether tennis is played. The root asks whether rain is expected. Yes predicts No. No leads to a strong-wind question: Yes predicts No and No predicts Yes. Questions use input features; leaves contain predictions.

Decision Trees Explained: How They Work

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Illustrative tree: rain predicts No; without rain, strong wind predicts No and calmer conditions predict Yes. This is a teaching example, not a measured weather model. View full-size diagram Reviewed and updated October 3, 2026. A decision tree is a supervised machine-learning model that learns a hierarchy of decision rules from training data, then follows

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Scatterplot of synthetic energy-use observations with a fitted regression line. At 125 square metres, the prediction is 410 kWh per month; an observed value of 480 creates a positive residual of 70.

Linear Regression Explained for Beginners

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Linear Regression is a supervised-learning method for predicting a numerical target from a linear combination of one or more input features. It is an excellent first algorithm to learn because every prediction can be traced to an equation, while its limitations introduce residuals, evaluation, diagnostics, and extrapolation. Last reviewed and updated: September 13, 2026 Illustrative

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Email features produce a score of 1.39 and predicted spam probability about 0.80. At the example threshold 0.50, the message is labelled spam.

Logistic Regression Explained for Beginners

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Logistic Regression is a supervised classification method that estimates the probability of a class. It starts with a linear score, passes that score through the sigmoid function, and—when a hard decision is needed—compares the probability with a chosen threshold. This guide uses spam detection as a running example. You will learn why the method has

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