Whether you are refreshing your ML fundamentals or preparing for a cloud or AI certification, it helps to revisit the core types of machine learning algorithms and where they shine.

ML algorithm table image

Let us summarize them:

🔹 Supervised Learning

Uses labeled data to predict outcomes.

➡️ Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, Gradient Boosting, Neural Networks.

💡 Use for: classification, regression, forecasting.

🔹 Unsupervised Learning

No labels — the system finds hidden patterns.

➡️ Algorithms: K-Means, Hierarchical Clustering, PCA, Autoencoders.

💡 Use for: clustering, dimensionality reduction, pattern discovery.

🔹 Semi-Supervised Learning

A mix of labeled and unlabeled data for better generalization.

➡️ Common in text and image applications where labeling is expensive.

🔹 Reinforcement Learning (RL)

The model learns by trial and reward.

➡️ Algorithms: Q-Learning, Deep Q-Networks (DQN), Policy Gradient.

💡 Use for: robotics, games, dynamic decision-making systems.

🔹 Self-Supervised & Generative Learning (modern trends)

Fueled by LLMs and diffusion models, where models generate or reconstruct data from context.

➡️ Algorithms: Transformers, GPT, BERT, Stable Diffusion, VAEs.

💡 Use for: natural language, image, and multimodal generation tasks.

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Let us detail them:

Supervised (prediction/classification)

Linear Regression – Predicts continuous values; simple & fast; avoid with strong non-linearity/outliers.

Logistic Regression – Binary classification; interpretable; weak with complex non-linear boundaries.

Decision Tree – Easy to interpret for classification/regression; can overfit on noisy/complex data.

Random Forest – Ensemble of trees; robust & accurate; slower, less interpretable; not for strict real-time.

Gradient Boosting (e.g., XGBoost) – High accuracy on tabular data; needs careful tuning; overfits on noisy data and when interpretability is key.

SVM – Works in high dimensions; slow on large datasets; needs scaling/separability.

KNN – Simple, few-shot classification; slow with many features/points; needs scaling.

Naive Bayes – Fast text classification; fails when features are strongly dependent.

Unsupervised (structure discovery)

K-Means – Fast clustering with spherical/equal clusters; needs K; struggles with non-spherical shapes & noise.

Hierarchical Clustering – Dendrograms, no need for K; memory/compute heavy; poor on very large data.

DBSCAN – Density-based clusters; handles noise & odd shapes; fails on widely varying densities/sparse high-dim.

Dimensionality Reduction

PCA – Compresses features via covariance eigenvectors; speeds models; hard to interpret; assumes variance ≈ signal.

Neural / Deep Learning

MLP (Neural Networks) – Flexible non-linear modeling; needs lots of data/tuning; less interpretable.

CNN – Best for images/spatial data; high compute; not for sequence/text by default.

RNN – Sequences/time series; can suffer vanishing gradients; struggles with long sequences.

Transformers (BERT/GPT) – Attention + positional encoding; excels on NLP/long context; data/compute hungry; complex.

Autoencoders – Compression/anomaly detection; effective but black-box; can overfit; not for required lossless compression.

Typical use cases (examples)

Regression: house prices (Linear Regression)

Binary classification: spam detection (Logistic)

Tabular accuracy: credit scoring (Gradient Boosting)

Vision: facial recognition (SVM/CNN)

Recommenders: KNN

Sentiment/Text: Naive Bayes, Transformers

Customer segments: K-Means

Gene/expression analysis: Hierarchical

Image compression: PCA / Autoencoders

Geo-spatial clustering: DBSCAN

Chat/translation: Transformers

Quick selection tips

Need interpretability & speed on tabular? Linear/Logistic/Tree.

Need robust accuracy on mixed tabular data? Random Forest / Gradient Boosting.

Lots of features, non-linear patterns? SVM (smaller data) or Boosting.

Images or spatial signals? CNN. Sequences? RNN/Transformer. Text/NLP? Transformer.

Compress or denoise? PCA/Autoencoder.

Find groups? K-Means (simple), DBSCAN (irregular shapes/noise), Hierarchical (small–medium data, dendrograms).