Logistic Regression:

• Logistic regression is a supervised learning algorithm used for binary classification tasks.
• It models the probability of the outcome using a logistic function and separates the classes based on a decision boundary.
• It is a linear algorithm that can handle both numerical and categorical input features.

Decision Trees:

• Decision trees are supervised learning algorithms used for classification and regression tasks.
• They partition the data based on feature conditions to create a tree-like model of decisions and their consequences.
• Decision trees are interpretable and can handle both numerical and categorical features.

Random Forest:

• Random Forest is an ensemble learning algorithm that combines multiple decision trees.
• It builds a collection of decision trees and makes predictions by aggregating the individual tree’s predictions.
• Random Forest is robust against overfitting and can handle high-dimensional data.

Support Vector Machines (SVM):

• SVM is a supervised learning algorithm used for classification and regression tasks.
• It separates classes by finding the hyperplane that maximizes the margin between them.
• SVM can handle both linear and non-linear decision boundaries using kernel functions.

Naive Bayes:

• Naive Bayes is a probabilistic supervised learning algorithm used for classification tasks.
• It is based on Bayes’ theorem and assumes independence between features given the class.
• Naive Bayes is efficient, particularly for high-dimensional data, but it makes a strong assumption of feature independence.

Regularization:

• Regularization is a technique used to prevent overfitting in machine learning models.
• It adds a penalty term to the model’s objective function, discouraging complex or extreme parameter values.
• Common regularization techniques include L1 (Lasso) and L2 (Ridge) regularization.

DBSCAN (Density-Based Spatial Clustering of Applications with Noise):

• DBSCAN is an unsupervised learning algorithm used for density-based clustering.
• It groups together data points that are close to each other and separates regions of lower density.
• DBSCAN can identify clusters of arbitrary shape and handle noise points.

Gaussian Mixture Model (GMM):

• GMM is a probabilistic unsupervised learning algorithm used for clustering and density estimation.
• It assumes that the data is generated from a mixture of Gaussian distributions.
• GMM assigns probabilities to each data point, allowing for soft clustering and handling overlapping clusters.