Clustering is a data mining technique that groups similar data points together into clusters.

Supervised machine learning algorithms learn from labeled data to make predictions on new, unseen data.

Support Vector Machines (SVMs) are widely used for classification tasks due to their ability to handle high-dimensional data and non-linear relationships.

Feature selection aims to remove irrelevant or redundant features, improve model accuracy, and reduce training time.

Principal Component Analysis (PCA) is a widely used unsupervised machine learning algorithm for dimensionality reduction.

Reinforcement learning algorithms learn by interacting with their environment and receiving rewards or punishments for their actions.

Deep Q-Network (DQN) is a popular reinforcement learning algorithm that combines deep learning with Q-learning.

Cross-validation is used to evaluate model performance, tune hyperparameters, and prevent overfitting.

F1-score is a widely used evaluation metric that considers both precision and recall.

Regularization aims to prevent overfitting, improve generalization performance, and reduce model variance.

L2 Regularization (Ridge) is a widely used regularization technique in linear regression.

Ensemble learning methods aim to combine the predictions of multiple models to improve accuracy, robustness, and reduce computational cost.

Random Forest is a widely used ensemble learning method that combines the predictions of multiple decision trees.

Hyperparameter tuning aims to find the optimal values of hyperparameters to improve model performance and prevent overfitting.

Grid Search is a simple and widely used method for hyperparameter tuning.