Machine learning is a vast field with various algorithms designed for different tasks. This paper provides an overview of some popular machine learning algorithms.

Algorithms

  • Supervised Learning Algorithms

    • Linear Regression: A linear model that predicts the value of a dependent variable based on one or more independent variables.
    • Logistic Regression: Used for binary classification problems, predicting the probability of the occurrence of an event.
    • Support Vector Machines (SVM): A powerful classifier that separates data points by finding the optimal hyperplane.

  • Unsupervised Learning Algorithms

    • K-Means Clustering: A method of partitioning data into k clusters.
    • Principal Component Analysis (PCA): A dimensionality reduction technique that transforms a dataset into a set of principal components.
    • Association Rules: Used to discover interesting relationships or patterns in large databases.

  • Reinforcement Learning Algorithms

    • Q-Learning: A value-based algorithm that learns the optimal action policy.
    • Policy Gradient: An algorithm that learns the optimal policy directly from the gradient of the expected reward.

Resources

For more detailed information on machine learning algorithms, you can visit our Machine Learning Tutorial.

Images

Machine Learning

Linear Regression

K-Means Clustering