Core Machine Learning Algorithms
Linear Regression
A fundamental supervised learning algorithm used for predicting a continuous target variable based on one or more predictor variables. It models the relationship between variables by fitting a linear equation to the observed data.
Logistic Regression
Despite its name, it's a classification algorithm used for predicting a binary outcome (e.g., yes/no, true/false). It uses a sigmoid function to map predictions to probabilities, which are then used to assign a class label.
Decision Trees
A flowchart-like structure where internal nodes represent tests on an attribute, branches represent the outcome of the test, and leaf nodes represent class labels (in classification) or a continuous value (in regression).
Random Forest
An ensemble learning method that constructs multiple decision trees during training and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees. It reduces overfitting and improves accuracy.
Support Vector Machines (SVM)
A powerful supervised learning algorithm for both classification and regression tasks. It works by finding the hyperplane that best separates data points of different classes in a high-dimensional space.
K-Nearest Neighbors (KNN)
A non-parametric, instance-based learning algorithm used for classification and regression. It classifies a new data point based on the majority class of its 'k' nearest neighbors in the feature space.
K-Means Clustering
A popular unsupervised learning algorithm used for partitioning a dataset into 'k' distinct clusters. It aims to minimize the within-cluster sum of squares, assigning each data point to the cluster whose mean is nearest.
Principal Component Analysis (PCA)
A dimensionality reduction technique used in unsupervised learning. PCA transforms data into a new coordinate system such that the greatest variance by any projection of the data lies on the first coordinate (the first principal component), the second greatest variance on the second coordinate, and so on.