Machine Learning Algorithms

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.

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.