Essential Machine Learning Algorithms: From Decision Trees to ICA Explained

Machine learning and AI have become increasingly popular, with big data driving powerful predictive models used by services like Netflix and Amazon.

If you want to start learning ML, the author shares personal experiences from university courses and online tutorials, then presents ten widely used algorithms.

Supervised Learning

1. Decision Trees

Decision trees are a visual decision‑support tool that models possible outcomes, costs, and utilities.

They help evaluate binary (yes/no) decisions and structure problem‑solving logically.

2. Naive Bayes Classification

Naive Bayes classifiers apply Bayes' theorem with a strong independence assumption between features.

Typical applications include spam detection, news categorization, sentiment analysis, and face recognition.

Spam detection

News categorization (technology, politics, sports)

Sentiment analysis (positive vs. negative)

Face recognition

3. Ordinary Least Squares Regression

Ordinary Least Squares (OLS) computes a linear regression line that minimizes the sum of squared vertical distances from data points to the line.

The method reduces the error metric while fitting a linear model.

4. Logistic Regression

Logistic regression estimates the probability of a binary outcome using one or more explanatory variables and the logistic function.

Real‑world uses include credit scoring, marketing campaign success prediction, product revenue forecasting, and earthquake occurrence estimation.

Credit scoring

Marketing campaign success rate

Product revenue prediction

Earthquake occurrence forecasting

5. Support Vector Machine (SVM)

SVM creates a hyperplane that separates data points of two classes with the maximum margin.

It scales to tasks such as display advertising, splice‑site recognition, gender detection from images, and large‑scale image classification.

6. Ensemble Methods

Ensemble methods combine multiple classifiers through weighted voting to improve performance.

They reduce bias, lower variance, and mitigate over‑fitting.

Unsupervised Learning

7. Clustering Algorithms

Clustering groups objects so that items in the same cluster are more similar to each other than to those in other clusters.

Examples include centroid‑based, connectivity‑based, density‑based, probabilistic, dimensionality‑reduction, and neural‑network/deep‑learning clustering.

8. Principal Component Analysis (PCA)

PCA transforms correlated variables into a set of linearly uncorrelated components, useful for compression, simplification, and visualization.

It is unsuitable when all components have high variance due to noisy data.

9. Singular Value Decomposition (SVD)

SVD factorizes a matrix M into UΣV, where U and V are unitary matrices and Σ is diagonal.

PCA can be viewed as a simplified application of SVD, and both are used in face‑recognition pipelines.

10. Independent Component Analysis (ICA)

ICA separates mixed signals into statistically independent components, useful when PCA fails.

Applications include image processing, document databases, economic indicators, and psychometrics.

Use these algorithms to build machine‑learning applications that improve experiences worldwide.