Machine learning algorithms are the core of a machine learning model. They act as the set of instructions that a computer uses to learn from data. These algorithms can be categorized into three main types. The categorization is based on the nature of the data and the learning process.
1. Supervised Learning Algorithms
Supervised learning is the most common type of machine learning, where the algorithm learns from a labeled dataset. The goal is to predict an output based on given input data. A “supervisor” (the labeled data) provides the correct answers during training.
For Classification (Predicting Categories) 🎯
- Logistic Regression: A simple but effective algorithm used for binary classification, predicting the probability of an event belonging to one of two classes (e.g., spam or not spam, yes or no).
- Support Vector Machines (SVM): This algorithm finds the best “hyperplane” or decision boundary to separate data points into different classes. It works well in high-dimensional spaces.
- Decision Tree: A tree-like model that makes decisions based on a series of if-then-else rules derived from the data. It’s easy to interpret and visualize.
- Random Forest: An ensemble algorithm that improves on the decision tree. It builds multiple decision trees and combines their predictions to produce a more accurate and stable result, reducing the risk of overfitting.
- K-Nearest Neighbors (KNN) – Classifies based on closest data points.
- Naive Bayes – Probabilistic classifier (great for spam filters).
- Gradient Boosting (XGBoost, LightGBM, CatBoost) – High-performance classifiers.
For Regression (Predicting Continuous Values) 📈
- Linear Regression: A foundational algorithm that finds the best-fitting straight line to model the relationship between a single input variable and a continuous output variable (e.g., predicting house prices based on square footage).
- Polynomial Regression: A more flexible version of linear regression that models a non-linear relationship by using a polynomial function.
- Ridge & Lasso Regression – Handle overfitting with regularization.
- Support Vector Regression (SVR).
2. Unsupervised Learning Algorithms
Unlike supervised learning, unsupervised learning algorithms are given unlabeled data and must find hidden patterns and structures on their own. The goal is to discover relationships in the data. It is about gaining insights without any pre-existing knowledge of the output.
For Clustering (Grouping Data) 📦
- K-Means: A popular algorithm that partitions data into a pre-defined number of clusters (K). It iteratively assigns each data point to the cluster with the nearest mean (or centroid).
- Hierarchical Clustering: This algorithm builds a tree-like structure of clusters (called a dendrogram) by either merging smaller clusters together (agglomerative) or splitting a large cluster (divisive). It doesn’t require you to specify the number of clusters beforehand.
- DBSCAN – Finds clusters of arbitrary shape.
For Dimensionality Reduction (Simplifying Data) 📊
- Principal Component Analysis (PCA): It is a technique that reduces the number of features in a dataset. It achieves this by finding a smaller set of “principal components”. These components capture the most important information. It’s often used for data visualization and to improve model performance.
- t-SNE – Visualizes high-dimensional data.
- Autoencoders – Neural networks for feature compression.
3. Semi-Supervised Learning Algorithms
- Use small labeled data + large unlabeled data.
- Examples: Semi-supervised SVMs, graph-based algorithms.
4. Reinforcement Learning Algorithms
In reinforcement learning, an agent learns to make decisions by taking actions in an environment to maximize a cumulative reward. It’s a trial-and-error process where the agent learns from the consequences of its actions.
- Q-Learning: A popular algorithm that learns the value of taking a specific action in a given state. It builds a “Q-table” to guide the agent toward the actions with the highest long-term reward.
- Deep Q-Networks (DQN): An advancement on Q-learning that uses deep neural networks to handle more complex, high-dimensional environments. It’s what powered AI to master classic Atari video games.
- Actor-Critic: This is a more sophisticated approach. It uses two neural networks. An Actor decides what action to take. A Critic evaluates the chosen action. This allows the model to learn more efficiently.
5. Deep Learning Algorithms (subset of ML using neural networks)
- Artificial Neural Networks (ANNs) – Basic deep models.
- Convolutional Neural Networks (CNNs) – Image recognition.
- Recurrent Neural Networks (RNNs) – Sequential data (speech, text).
- Long Short-Term Memory (LSTM) – Captures long-term dependencies.
- Transformers (BERT, GPT, T5) – Advanced NLP.
- Generative Adversarial Networks (GANs) – Data generation.
6. Ensemble Learning Algorithms
Combine multiple models to improve accuracy.
- Bagging – Example: Random Forest.
- Boosting – Example: AdaBoost, XGBoost, LightGBM.
- Stacking – Combines multiple learners for stronger prediction.
✅ Quick Reference Table
| Category | Algorithms Examples |
|---|---|
| Supervised (Regression) | Linear Regression, Lasso, Ridge, SVR |
| Supervised (Classification) | Logistic Regression, SVM, Decision Trees, Random Forest, Naive Bayes, KNN |
| Unsupervised (Clustering) | K-Means, Hierarchical, DBSCAN |
| Unsupervised (Dim. Reduction) | PCA, t-SNE, Autoencoders |
| Semi-Supervised | Semi-supervised SVM, Graph-based methods |
| Reinforcement Learning | Q-Learning, SARSA, DQN, Policy Gradients |
| Deep Learning | ANN, CNN, RNN, LSTM, Transformers, GANs |
| Ensemble Methods | Bagging, Boosting, Stacking |
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