Linear Regression: This is a workhorse algorithm that predicts a continuous value by establishing a linear relationship between input variables and the output.

Logistic Regression: This builds on linear regression for classification tasks, predicting the probability of an event falling into a specific category.

Decision Tree: This algorithm creates a tree-like structure to classify data by asking a series of questions about the features.

Support Vector Machine (SVM): SVMs excel at finding the optimal hyperplane to differentiate data points belonging to different classes.

Random Forest: This powerful ensemble technique combines multiple decision trees to enhance accuracy and reduce overfitting.

K-Nearest Neighbors (KNN): KNN classifies data points based on the majority vote of their nearest neighbors.

Naive Bayes: This probabilistic classifier works well for situations where features are independent of each other.

K-Means: A popular clustering algorithm that groups data points into a predefined number of clusters based on similarities.

Gradient Boosting: This family of algorithms involves iteratively building models to improve on the predictions of previous models. XGBoost is a particularly noteworthy example.

Stochastic Gradient Descent (SGD): A fundamental optimization algorithm used in training various ML models.

Apriori: This unsupervised learning algorithm is commonly used for association rule learning, uncovering frequent itemsets in transactional data.