1. Linear Regression

Type: Supervised Learning (Regression)

A fundamental algorithm for modeling the linear relationship between a dependent variable and one or more independent variables.

2. Logistic Regression

Type: Supervised Learning (Classification)

Despite its name, it’s a classification algorithm used to predict the probability of a binary outcome (e.g., yes/no, true/false).

3. Decision Trees

A tree-like structure where each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a regression value. They are interpretable and can handle both categorical and numerical data.

4. Random Forest

An ensemble learning method that builds multiple decision trees and merges their predictions to get a more accurate and stable prediction. It helps to reduce overfitting.

5. Support Vector Machines (SVM)

An algorithm that finds the optimal hyperplane that best separates data points into different classes. It can also be used for regression tasks.

6. K-Nearest Neighbors (KNN)

A simple algorithm that classifies a new data point based on the majority class of its k-nearest neighbors in the feature space.

7. K-Means Clustering

Type: Unsupervised Learning (Clustering)

A popular clustering algorithm that partitions n observations into k clusters, where each observation belongs to the cluster with the nearest mean (cluster center).

8. Principal Component Analysis (PCA)

Type: Unsupervised Learning (Dimensionality Reduction)

A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.

9. Naive Bayes

Type: Supervised Learning (Classification)

A probabilistic classifier based on Bayes’ theorem with the “naive” assumption of independence between every pair of features.

10. Gradient Boosting Algorithms (e.g., XGBoost, LightGBM, CatBoost)

Powerful ensemble learning techniques that build models in a stage-wise fashion, where each new model corrects the errors made by the previous ones. They often achieve state-of-the-art results on structured data.

11. Neural Networks and Deep Learning

Inspired by the human brain, neural networks are composed of interconnected nodes (neurons) organized in layers. Deep learning involves neural networks with multiple layers, enabling them to learn complex patterns from large amounts of data.