Logistic regression is a supervised learning algorithm used for binary classification tasks, where the output is a probability between 0 and 1.

Linear regression is a supervised learning algorithm used for regression tasks, where the output is a continuous value.

K-means clustering is an unsupervised learning algorithm used for clustering tasks, where the goal is to group similar data points together.

Principal component analysis (PCA) is a dimensionality reduction technique that identifies the principal components of a dataset, which are the directions of maximum variance in the data.

Q-learning is a reinforcement learning algorithm that learns a policy that maximizes the long-term reward by updating the Q-values of states and actions.

One-class support vector machine (OCSVM) is an anomaly detection algorithm that learns a boundary around the normal data points and identifies data points that fall outside the boundary as anomalies.

Recurrent neural networks (RNNs) are a type of neural network that is well-suited for natural language processing tasks, as they can learn from sequential data.

Convolutional neural networks (CNNs) are a type of neural network that is well-suited for image classification and object detection tasks, as they can learn from the spatial relationships between pixels in an image.

Collaborative filtering is a recommender system algorithm that predicts the preferences of a user based on the preferences of other users who have similar tastes.

Autoregressive integrated moving average (ARIMA) is a time series forecasting algorithm that models the time series as a combination of autoregressive, integrated, and moving average components.

Grid search is a hyperparameter tuning algorithm that evaluates all possible combinations of hyperparameter values within a specified range.

Filter methods are feature selection algorithms that evaluate the features independently and select the most informative ones based on a statistical measure.

Accuracy is a model evaluation metric that measures the proportion of correct predictions made by a machine learning model.

Bagging is an ensemble learning algorithm that trains multiple machine learning models on different subsets of the training data and combines their predictions by averaging.

Fine-tuning is a transfer learning technique that involves adjusting the weights of a pre-trained model on a new dataset.